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MRI imaging primarily focuses on the soft tissues of the human body, typically performed prior to a patient's transfer to the surgical suite for a medical procedure. However, utilizing MRI images for tumor diagnosis is a time-consuming process. To address these challenges, a new method for automatic brain tumor diagnosis was developed, employing a combination of image segmentation, feature extraction, and classification techniques to isolate the specific region of interest in an MRI image corresponding to a brain tumor. The proposed method in this study comprises five distinct steps. Firstly, image pre-processing is conducted, utilizing various filters to enhance image quality. Subsequently, image thresholding is applied to facilitate segmentation. Following segmentation, feature extraction is performed, analyzing morphological and structural properties of the images. Then, feature selection is carried out using principal component analysis (PCA). Finally, classification is performed using an artificial neural network (ANN). In total, 74 unique features were extracted from each image, resulting in a dataset of 144 observations. Principal component analysis was employed to select the top 8 most effective features. Artificial Neural Networks (ANNs) leverage comprehensive data and selective knowledge. Consequently, the proposed approach was evaluated and compared with alternative methods, resulting in significant improvements in precision, accuracy, and F1 score. The proposed method demonstrated notable increases in accuracy, with improvements of 99.3%, 97.3%, and 98.5% in accuracy, Sensitivity and F1 score. These findings highlight the efficiency of this approach in accurately segmenting and classifying MRI images. [ABSTRACT FROM AUTHOR]

This paper presents a prototype of a portable and modular electrical impedance tomography (EIT) system for breast tumor detection. The proposed system uses MATLAB to generate three-dimensional representations of breast tissue. The modular architecture of the system allows for flexible customization and scalability. It consists of several interconnected modules. Each module can be easily replaced or upgraded, facilitating system maintenance and future enhancements. Testing of the prototype has shown promising results in preliminary screening based on experimental studies. Agar models were used for the experimental stage of this project. The 3D representations provide clinicians with valuable information for accurate diagnosis and treatment planning. Further research and refinement of the system is warranted to validate its performance in future clinical trials. [ABSTRACT FROM AUTHOR]

Breast cancer, the earliest documented cancer in history, stands as a foremost cause of mortality, accounting for 684,996 deaths globally in 2020 (15.5% of all female cancer cases). Irrespective of socioeconomic factors, geographic locations, race, or ethnicity, breast cancer ranks as the most frequently diagnosed cancer in women. The standard grading for breast cancer utilizes the Nottingham Histopathology Grading (NHG) system, which considers three crucial features: mitotic counts, nuclear pleomorphism, and tubule formation. Comprehensive reviews on features, for example, mitotic count and nuclear pleomorphism have been available thus far. Nevertheless, a thorough investigation specifically focusing on tubule formation aligned with the NHG system is currently lacking. Motivated by this gap, the present study aims to unravel tubule formation in histopathology images via a comprehensive review of detection approaches involving tubule and tumor features. Without temporal constraints, a structured methodology is established in line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, resulting in 12 articles for tubule detection and 67 included articles for tumor detection. Despite the primary focus on breast cancer, the structured search string extends beyond this domain to encompass any cancer type utilizing histopathology images as input, focusing on tubule and tumor detection. This broadened scope is essential. Insights from approaches in tubule and tumor detection for various cancers can be assimilated, integrated, and contributed to an enhanced understanding of tubule formation in breast histopathology images. This study compiles evidence-based analyses into a cohesive document, offering comprehensive information to a diverse audience, including newcomers, experienced researchers, and stakeholders interested in the subject matter. [ABSTRACT FROM AUTHOR]

Detection of circulating tumor DNA (ctDNA) mutations, which are molecular biomarkers present in bodily fluids of cancer patients, can be applied for tumor diagnosis and prognosis monitoring. However, current profiling of ctDNA mutations relies primarily on polymerase chain reaction (PCR) and DNA sequencing and these techniques require preanalytical processing of blood samples, which are time‐consuming, expensive, and tedious procedures that increase the risk of sample contamination. To overcome these limitations, here the engineering of a DNA/γPNA (gamma peptide nucleic acid) hybrid nanoreporter is disclosed for ctDNA biosensing via in situ profiling and recording of tumor‐specific DNA mutations. The low tolerance of γPNA to single mismatch in base pairing with DNA allows highly selective recognition and recording of ctDNA mutations in peripheral blood. Owing to their remarkable biostability, the detached γPNA strands triggered by mutant ctDNA will be enriched in kidneys and cleared into urine for urinalysis. It is demonstrated that the nanoreporter has high specificity for ctDNA mutation in peripheral blood, and urinalysis of cleared γPNA can provide valuable information for tumor progression and prognosis evaluation. This work demonstrates the potential of the nanoreporter for urinary monitoring of tumor and patient prognosis through in situ biosensing of ctDNA mutations. [ABSTRACT FROM AUTHOR]

Tumors are most dangerous to humans and cause death when patient not noticed it in the early stages. Edema is one type of brain swelling that consists of toxic particles in the human brain. Especially in the brain, the tumors are identified with magnetic resonance imaging (MRI) scanning. This scanning plays a major role in detecting the area of the affected area in the given input image. Tumors may contain cancer or non-cancerous cells. Many experts have used this MRI report as the primary confirmation of the tumors or edemas as cancer cells. Brain tumor segmentation is a significant task that is used to classify the normal and tumor tissues. In this paper, a hybrid segmentation approach (HSA) is introduced to detect the accurate regions of tumors and edemas to the given brain input image. HSA is the combination of an advanced segmentation model and edge detection technique used to find the state of the tumors or edemas. HSA is applied on the Kaggle brain image dataset consisting of MRI scanning images. Edge detection technique improves the detection of tumor or edema region. The performance of the HSA is compared with various algorithms such as Fully Automatic Heterogeneous Segmentation using support vector machine (FAHS-SVM), SVM with Normal Segmentation, etc. Performance of proposed work is calculated using mean square error (MSE), peak signal noise ratio (PSNR), and accuracy. The proposed approach achieved better performance by improving accuracy. [ABSTRACT FROM AUTHOR]

Brain tumor segmentation is paramount in medical diagnostics. This study presents a multistage segmentation model consisting of two main steps. First, the fusion of magnetic resonance imaging (MRI) modalities creates new and more effective tumor imaging modalities. Second, the semantic segmentation of the original and fused modalities, utilizing various modified architectures of the U‐Net model. In the first step, a residual network with multi‐scale backbone architecture (Res2Net) and guided filter are employed for pixel‐by‐pixel image fusion tasks without requiring any training or learning process. This method captures both detailed and base elements from the multimodal images to produce better and more informative fused images that significantly enhance the segmentation process. Many fusion scenarios were performed and analyzed, revealing that the best fusion results are attained when combining T2‐weighted (T2) with fluid‐attenuated inversion recovery (FLAIR) and T1‐weighted contrast‐enhanced (T1CE) with FLAIR modalities. In the second step, several models, including the U‐Net and its many modifications (adding attention layers, residual connections, and depthwise separable connections), are trained using both the original and fused modalities. Further, a "Model Selection‐based" fusion of these individual models is also considered for more enhancement. In the preprocessing step, the images are resized by cropping them to decrease the pixel count and minimize background interference. Experiments utilizing the brain tumor segmentation (BraTS) 2020 dataset were performed to verify the efficiency and accuracy of the proposed methodology. The "Model Selection‐based" fusion model achieved an average Dice score of 88.4%, an individual score of 91.1% for the whole tumor (WT) class, an average sensitivity score of 86.26%, and a specificity score of 91.7%. These results prove the robustness and high performance of the proposed methodology compared to other state‐of‐the‐art methods. [ABSTRACT FROM AUTHOR]

This review focuses on the pivotal role of radiotracers in breast cancer imaging, emphasizing their importance in accurate detection, staging, and treatment monitoring. Radiotracers, labeled with radioactive isotopes, are integral to various nuclear imaging techniques, including positron emission tomography (PET) and positron emission mammography (PEM). The most widely used radiotracer in breast cancer imaging is 18F-fluorodeoxyglucose (18F-FDG), which highlights areas of increased glucose metabolism, a hallmark of many cancer cells. This allows for the identification of primary tumors and metastatic sites and the assessment of tumor response to therapy. In addition to 18F-FDG, this review will explore newer radiotracers targeting specific receptors, such as estrogen receptors or HER2, which offer more personalized imaging options. These tracers provide valuable insights into the molecular characteristics of tumors, aiding in tailored treatment strategies. By integrating radiotracers into breast cancer management, clinicians can enhance early disease detection, monitor therapeutic efficacy, and guide interventions, ultimately improving patient outcomes. Ongoing research aimed at developing more specific and sensitive tracers will also be highlighted, underscoring their potential to advance precision medicine in breast cancer care. [ABSTRACT FROM AUTHOR]

Background: Despite advances in treatment, residual or recurrent tumors after definitive (chemo) radiotherapy for laryngeal and hypopharyngeal squamous cell carcinoma (SCC) remain a challenge in clinical management and require accurate and timely detection for optimal salvage therapy. This study aimed to compare the diagnostic value of Fluorine 18 (18F) fluorodeoxyglucose (FDG) positron emission tomography (PET)/computed tomography (CT) and diffusion‐weighted magnetic resonance imaging (DW‐MRI) in detecting residual or recurrent tumors after definitive (chemo) radiotherapy for laryngeal and hypopharyngeal SCC. Methods: A prospective study was conducted on 30 patients who presented with new symptoms after definitive (chemo) radiotherapy for laryngeal (n = 21) and hypopharyngeal (n = 9) carcinoma. Both 18F‐FDG PET/CT and DW‐MRI were performed and histopathologic analysis served as the standard of reference. Results: Histopathology showed 20 patients as positive and 10 as negative for tumors. 18F‐FDG PET/CT detected all tumors correctly but was falsely positive in one case. DW‐MRI detected tumors in 18 out of 20 positive patients and correctly excluded tumors in all negative patients. The sensitivity and specificity of 18F‐FDG PET/CT were 100% and 90%, respectively, while the values for DW‐MRI were 90% and 100%, respectively. Conclusions: The study concludes that 18F‐FDG PET/CT is slightly superior to DW‐MRI in detecting residual or recurrent tumors after definitive (chemo) radiotherapy for laryngeal and hypopharyngeal SCC. The combined use of 18F‐FDG PET/CT and DW‐MRI can potentially improve specificity in therapy response evaluation. [ABSTRACT FROM AUTHOR]

Fluorescence guidance is routinely used in surgery to enhance perfusion contrast in multiple types of diseases. Pressure-enhanced sensing of tissue oxygenation (PRESTO) via fluorescence is a technique extensively analyzed here, that uses an FDA-approved human precursor molecule, 5-aminolevulinic acid (ALA), to stimulate a unique delayed fluorescence signal that is representative of tissue hypoxia. The ALA precontrast agent is metabolized in most tissues into a red fluorescent molecule, protoporphyrin IX (PpIX), which has both prompt fluorescence, indicative of the concentration, and a delayed fluorescence, that is amplified in low tissue oxygen situations. Applied pressure from palpation induces transient capillary stasis and a resulting transient PRESTO contrast, dominant when there is near hypoxia. This study examined the kinetics and behavior of this effect in both normal and tumor tissues, with a prolonged high PRESTO contrast (contrast to background of 7.3) across 5 tumor models, due to sluggish capillaries and inhibited vasodynamics. This tissue function imaging approach is a fundamentally unique tool for real-time palpation-induced tissue response in vivo, relevant for chronic hypoxia, such as vascular diseases or oncologic surgery. [ABSTRACT FROM AUTHOR]

Designing dynamic assemblies in living cells is crucial for creating organelle‐like structures, yet precisely controlling their morphological transitions in response to specific signals is a significant challenge. In this study, a DNA framework is combined with hybridization chain reaction (HCR) to achieve specific assembly of hyperbranched aggregates in cancer cells. HCR, distinguished for its signal amplification and linear extension capabilities, enables the morphological transition of precursors to be specifically triggered by trace amounts of endogenous microRNA‐21 (miR‐21). The spatial constraints of the framework and the diversity of hairpin orientations significantly accelerate the assembly kinetics of hyperbranched networks, and the resulting micrometer‐scale aggregates possess enhanced intracellular retention capabilities. Introducing Ce6 molecules as a proof of concept, the regulatory function of aggregates can be activated under light irradiation and remains effective over a long period. The probe we constructed demonstrates good stability and biocompatibility, offers easy functionalization, and works inside cells long‐term, making it an ideal candidate material for the construction of organelle‐like structures. [ABSTRACT FROM AUTHOR]

Tumor detection in automated breast ultrasound (ABUS) images is critical for computer-aided diagnosis. A single deep learning based network is prone to make overconfident prediction. In this paper, a DML-YOLOX model based on deep mutual learning (DML) is proposed. To alleviate the overconfidence of a single network, the dual-model collaborative training strategy is designed based on YOLOX baseline. To implement the interaction between the dual models, the exploration loss is developed and combined with the consistency loss for supervision. The exploration loss aims at encouraging the models to explore and learn different feature representations in the feature extraction stage. The consistency loss aims at constraining the models to have consistent output representations for the same input. According to the ellipse-like characteristic of ABUS tumors, a rotation augmentation method is designed, which can decrease the overestimate of the bounding box. Besides, in order to reduce the regression error caused by large angle rotation, a rotation discriminant (RD) measurement is developed. The proposed method is verified on two ABUS datasets, one of which is the private ABUS dataset with 68 normal volumes and 68 tumor volumes, including 43,248 slices, and the other is the public ABUS dataset. On the private ABUS dataset, it achieves a promising detection result with a sensitivity of 0.90 and the false positives per slice (FPs/S) at 0.15. [ABSTRACT FROM AUTHOR]

This study designs a microstrip patch antenna with an inverted T-type notch in the partial ground to detect tumor cells inside the human breast. The size of the current antenna is small enough (18 mm × 21 mm × 1.6 mm) to distribute around the breast phantom. The operating frequency has been observed from 6–14 GHz with a minimum return loss of −61.18 dB and the maximum gain of current proposed antenna is 5.8 dBi which is flexible with respect to the size of antenna. After the distribution of eight antennas around the breast phantom, the return loss curves were observed in the presence and absence of tumor cells inside the breast phantom, and these observations show a sharp difference between the presence and absence of tumor cells. The simulated results show that this proposed antenna is suitable for early detection of cancerous cells inside the breast. Graphic Abstract [ABSTRACT FROM AUTHOR]

Abstract MRI imaging primarily focuses on the soft tissues of the human body, typically performed prior to a patient's transfer to the surgical suite for a medical procedure. However, utilizing MRI images for tumor diagnosis is a time-consuming process. To address these challenges, a new method for automatic brain tumor diagnosis was developed, employing a combination of image segmentation, feature extraction, and classification techniques to isolate the specific region of interest in an MRI image corresponding to a brain tumor. The proposed method in this study comprises five distinct steps. Firstly, image pre-processing is conducted, utilizing various filters to enhance image quality. Subsequently, image thresholding is applied to facilitate segmentation. Following segmentation, feature extraction is performed, analyzing morphological and structural properties of the images. Then, feature selection is carried out using principal component analysis (PCA). Finally, classification is performed using an artificial neural network (ANN). In total, 74 unique features were extracted from each image, resulting in a dataset of 144 observations. Principal component analysis was employed to select the top 8 most effective features. Artificial Neural Networks (ANNs) leverage comprehensive data and selective knowledge. Consequently, the proposed approach was evaluated and compared with alternative methods, resulting in significant improvements in precision, accuracy, and F1 score. The proposed method demonstrated notable increases in accuracy, with improvements of 99.3%, 97.3%, and 98.5% in accuracy, Sensitivity and F1 score. These findings highlight the efficiency of this approach in accurately segmenting and classifying MRI images.

Abstract Background Properly designed second near-infrared (NIR-II) nanoplatform that is responsive tumor microenvironment can intelligently distinguish between normal and cancerous tissues to achieve better targeting efficiency. Conventional photoacoustic nanoprobes are always “on”, and tumor microenvironment-responsive nanoprobe can minimize the influence of endogenous chromophore background signals. Therefore, the development of nanoprobe that can respond to internal tumor microenvironment and external stimulus shows great application potential for the photoacoustic diagnosis of tumor. Results In this work, a low-pH-triggered thermal-responsive volume phase transition nanogel gold nanorod@poly(n-isopropylacrylamide)-vinyl acetic acid (AuNR@PNIPAM-VAA) was constructed for photoacoustic detection of tumor. Via an external near-infrared photothermal switch, the absorption of AuNR@PNIPAM-VAA nanogel in the tumor microenvironment can be dynamically regulated, so that AuNR@PNIPAM-VAA nanogel produces switchable photoacoustic signals in the NIR-II window for tumor-specific enhanced photoacoustic imaging. In vitro results show that at pH 5.8, the absorption and photoacoustic signal amplitude of AuNR@PNIPAM-VAA nanogel in NIR-II increases up obviously after photothermal modulating, while they remain slightly change at pH 7.4. Quantitative calculation presents that photoacoustic signal amplitude of AuNR@PNIPAM-VAA nanogel at 1064 nm has ~ 1.6 folds enhancement as temperature increases from 37.5 °C to 45 °C in simulative tumor microenvironment. In vivo results show that the prepared AuNR@PNIPAM-VAA nanogel can achieve enhanced NIR-II photoacoustic imaging for selective tumor detection through dynamically responding to thermal field, which can be precisely controlled by external light. Conclusions This work will offer a viable strategy for the tumor-specific photoacoustic imaging using NIR light to regulate the thermal field and target the low pH tumor microenvironment, which is expected to realize accurate and dynamic monitoring of tumor diagnosis and treatment.

Background Diffusion-weighted MRI (DWI) offers a non-invasive approach to detect tumors based on water mobility differences from surrounding tissue. However, reliably discriminating malignancies from benign adipose remains challenging, especially in anatomical regions with abundant fat. The biophysical similarities between tumors and lipid signals create signal ambiguities that limit diagnostic accuracy using conventional reconstruction techniques. Methods We propose a novel low-rank reconstruction framework that combines accelerated diffusion data acquisition, structured low-rank regularization, and deep learning-assisted radiomic analysis to enhance fat-tumor discrimination in DWI. Simultaneous multi-slice imaging and controlled aliasing enable high spatiotemporal resolution while maintaining feasible scan times. A data-driven annihilating filter kernel is then learned from the undersampled data, imposing implicit low-rank constraints to suppress confounding fat signals while retaining tumor texture details during k-space reconstruction. Subsequent radiomic analysis extracts morphological imaging biomarkers from the reconstructed volumes to identify distinctive tumor signatures. Results Comprehensive validation on clinical DWI datasets demonstrates the improved fat-tumor discrimination capability of the proposed framework compared to conventional techniques. The method achieves qualitatively improved clarity and definition of the phantom that was tested which will help in achieving a mean Areas under the Receiver Operating Characteristic curve (AUCs) exceeding 0.80 for distinguishing malignant lesions from adipose tissue. Case studies illustrate how better signal specificity enables more confident clinical decisions. Conclusions The integrated low-rank reconstruction and radiomic analysis framework offers a promising solution to the longstanding problem of fat-tumor ambiguity in diffusion MRI. By unleashing the full diagnostic potential of DWI, this methodology can enhance non-invasive cancer screening and monitoring across diverse patient populations and anatomical regions.

Brain tumor classification using medical images is crucial for identification and therapy. However, brain tumors are complex and vary, making grouping them difficult. This work demonstrates a novel transfer learning method for brain tumor classification. We employ trained Convolutional Neural Networks (CNNs) models and data enrichment approaches to extract meaningful information from medical images. We want to fine-tune the models built on our dataset to uncover hierarchical patterns that distinguish tumor types. Through data enrichment, the training sample becomes more diverse and richer, making the model more generic and robust. Our team's extensive testing and research have shown that the suggested procedure can identify brain tumors. Our machine-learning approach performs better than others in terms of accuracy, sensitivity, specificity, and precision. Our technique improves brain tumor categorization and assures accurate clinical diagnosis. Automated testing systems are one way for physicians to assist patients in selecting the best course of treatment. Researchers may improve classification performance by incorporating modern imaging technology or topic-specific data. The Internet of Things, or IoT, is helping to drive the development of complex real-time data collection, processing, and sharing systems. These technological advancements have transformed medical imaging. This graphic depicts a cutting-edge transfer learning system that may be able to identify brain cancer from medical photos. This technology has the potential to enhance data collection and processing via the Internet of Things. Data augmentation and pre-trained convolutional neural networks may help to extract interpretable medical images. The Internet of Things improved the model's flexibility, resilience, and utility. We achieved this by expanding the training data set. Rapid categorization advancements have made clinical diagnosis more efficient. Classification, deep learning, medical imaging, machine learning, transfer learning, tumor detection, and image analysis all relate to this topic. [ABSTRACT FROM AUTHOR]

IR780 iodide is a commercially available targeted near‐infrared contrast agent for in vivo imaging and cancer photodynamic or photothermal therapy, whereas the accumulation, dynamics, and retention of IR780 in biological tissue, especially in tumor is still under‐explored. Diffuse fluorescence tomography (DFT) can be used for localization and quantification of the three‐dimensional distribution of NIR fluorophores. Herein, a homemade DFT imaging system combined with tumor‐targeted IR780 was utilized for cancer imaging and pharmacokinetic evaluation. The aim of this study is to comprehensively assess the biochemical and pharmacokinetic characteristics of IR780 with the aid of DFT imaging. The optimal IR780 concentration (20 μg/mL) was achieved first. Subsequently, the good biocompatibility and cellar uptake of IR780 was demonstrated through the mouse acute toxic test and cell assay. In vivo, DFT imaging effectively identified various subcutaneous tumors and revealed the long‐term retention of IR780 in tumors and rapid metabolism in the liver. Ex vivo imaging indicated IR780 was mainly concentrated in tumor and lung with significantly different from the distribution in other organs. DFT imaging allowed sensitive tumor detection and pharmacokinetic rates analysis. Simultaneously, the kinetics of IR780 in tumors and liver provided more valuable information for application and development of IR780. [ABSTRACT FROM AUTHOR]

Abstract Detection of circulating tumor DNA (ctDNA) mutations, which are molecular biomarkers present in bodily fluids of cancer patients, can be applied for tumor diagnosis and prognosis monitoring. However, current profiling of ctDNA mutations relies primarily on polymerase chain reaction (PCR) and DNA sequencing and these techniques require preanalytical processing of blood samples, which are time‐consuming, expensive, and tedious procedures that increase the risk of sample contamination. To overcome these limitations, here the engineering of a DNA/γPNA (gamma peptide nucleic acid) hybrid nanoreporter is disclosed for ctDNA biosensing via in situ profiling and recording of tumor‐specific DNA mutations. The low tolerance of γPNA to single mismatch in base pairing with DNA allows highly selective recognition and recording of ctDNA mutations in peripheral blood. Owing to their remarkable biostability, the detached γPNA strands triggered by mutant ctDNA will be enriched in kidneys and cleared into urine for urinalysis. It is demonstrated that the nanoreporter has high specificity for ctDNA mutation in peripheral blood, and urinalysis of cleared γPNA can provide valuable information for tumor progression and prognosis evaluation. This work demonstrates the potential of the nanoreporter for urinary monitoring of tumor and patient prognosis through in situ biosensing of ctDNA mutations.

Tumors rank among the most prevalent and lethal diseases worldwide, emphasizing the paramount importance of early detection in mitigating mortality rates. There has been a tremendous growth in medical image segmentation and classification for diagnosing cancer. This led to the various developments in medical research that help in diagnosing types of cancers and other human medical problems. This work focuses on performing effective segmentation procedures and feature extraction by a proposed hybrid intelligent technique for detecting tumors in the medical images. Additionally, the research addresses the design aspects of feature extraction, image classification, and texture features. These features are reduced by employing feature selection. Finally, experiments were conducted to compare tumor detection in medical images using the proposed algorithm with other existing algorithms. The results demonstrated promising achievements in terms of their evaluation metrics.

A comprehensive understanding of a tumor is required for accurate diagnosis and effective treatment. However, currently, there is no single imaging modality that can provide sufficient information. Photoacoustic (PA) imaging is a hybrid imaging technique with high spatial resolution and detection sensitivity, which can be combined with ultrasound (US) imaging to provide both optical and acoustic contrast. Elastography can noninvasively map the elasticity distribution of biological tissue, which reflects pathological conditions. In this study, we incorporated PA elastography into a commercial US/PA imaging system to develop a tri-modality imaging system, which has been tested for tumor detection using four mice with different physiological conditions. The results show that this tri-modality imaging system can provide complementary information on acoustic, optical, and mechanical properties. The enabled visualization and dimension estimation of tumors can lead to a more comprehensive tissue characterization for diagnosis and treatment.

There is no doubt that brain tumors are one of the leading causes of death in the world. A biopsy is considered the most important procedure in cancer diagnosis, but it comes with drawbacks, including low sensitivity, risks during biopsy treatment, and a lengthy wait for results. Early identification provides patients with a better prognosis and reduces treatment costs. The conventional methods of identifying brain tumors are based on medical professional skills, so there is a possibility of human error. The labor-intensive nature of traditional approaches makes healthcare resources expensive. A variety of imaging methods are available to detect brain tumors, including magnetic resonance imaging (MRI) and computed tomography (CT). Medical imaging research is being advanced by computer-aided diagnostic processes that enable visualization. Using clustering, automatic tumor segmentation leads to accurate tumor detection that reduces risk and helps with effective treatment. This study proposed a better Fuzzy C-Means segmentation algorithm for MRI images. To reduce complexity, the most relevant shape, texture, and color features are selected. The improved Extreme Learning machine classifies the tumors with 98.56% accuracy, 99.14% precision, and 99.25% recall. The proposed classifier consistently demonstrates higher accuracy across all tumor classes compared to existing models. Specifically, the proposed model exhibits accuracy improvements ranging from 1.21% to 6.23% when compared to other models. This consistent enhancement in accuracy emphasizes the robust performance of the proposed classifier, suggesting its potential for more accurate and reliable brain tumor classification. The improved algorithm achieved accuracy, precision, and recall rates of 98.47%, 98.59%, and 98.74% on the Fig share dataset and 99.42%, 99.75%, and 99.28% on the Kaggle dataset, respectively, which surpasses competing algorithms, particularly in detecting glioma grades. The proposed algorithm shows an improvement in accuracy, of approximately 5.39%, in the Fig share dataset and of 6.22% in the Kaggle dataset when compared to existing models. Despite challenges, including artifacts and computational complexity, the study's commitment to refining the technique and addressing limitations positions the improved FCM model as a noteworthy advancement in the realm of precise and efficient brain tumor identification. [ABSTRACT FROM AUTHOR]

In this study, a patch antenna is designed for easy fabrication and effective operation across a wide frequency range, making it particularly valuable for detecting brain tumors within a human head phantom using the monostatic technique. The study presents the proposed antenna's modeling in four stages, analyzing all essential parameters where a substrate material consisting of FR 4 is used. The antenna has a patch area of 0.27 λ × 0.21 λ and operates at 7.5 (free space), 7.48, and 7.47 GHz with a wide bandwidth of 3.188 GHz (free space), whereas in measurement, it operates in free space at 6.79 GHz with a bandwidth of 3.24 GHz, which is also categorized as an ultra-wide band. A comparative analysis is carried out between the proposed patch antenna and previous studies utilizing FR4 and similar substrate materials to enhance understanding. In this research, the analysis includes a human head phantom model, assessing key performance criteria such as SAR, return loss, and VSWR. The time-dependent analysis provides in this study insights into the dynamic behavior of the system in detecting brain tumor. The proposed antenna has been evaluated and yielded a maximum specific absorption rate (SAR) of 0.420985 W/kg for 1 gram of tissue in the head phantom. The performance of the patch antenna is evaluated using CST software, while the fabricated PCB antenna is tested in real-world conditions to ensure dependable operation in practical applications. The main challenge addressed in this work is achieving accurate brain tumor detection within a human head phantom model in a CST environment and using a UWB patch antenna fabricated via PCB technology.

In order to improve patient outcomes, brain tumors—which are notorious for their catastrophic effects and short life expectancy, particularly in higher grades—need to be diagnosed accurately and treated with care. Patient survival chances may be hampered by incorrect medical procedures brought on by a brain tumor misdiagnosis. CNNs and computer-aided tumor detection systems have demonstrated promise in revolutionizing brain tumor diagnostics through the application of ML techniques. One issue in the field of brain tumor detection and classification is the dearth of non-invasive indication support systems, which is compounded by data scarcity. Conventional neural networks may cause problems such as overfitting and gradient vanishing when they use uniform filters in different visual settings. Moreover, these methods incur time and computational complexity as they train the model from scratch and extract the pertinent characteristics. This paper presents an InceptionV4 neural network architecture-based Transfer Learning-based methodology to address the shortcomings in brain tumor classification methods. The goal is to deliver precise diagnostic assistance while minimizing calculation time and improving accuracy. The model makes use of a dataset that contains 7022 MRI images that were obtained from figshare, the SARTAJ dataset, and Br35H, among other sites. The suggested InceptionV4 architecture improves its ability to categorize brain tumors into three groups and normal brain images by utilizing transfer learning approaches. The suggested InceptionV4 model achieves an accuracy rate of 98.7% in brain tumor classification, indicating the model’s remarkable performance. This suggests a noteworthy progression in the precision of diagnosis and computational effectiveness to support practitioners making decisions.

Accurate tumor detection and classification are crucial for cancer diagnosis and treatment. Traditional medical image analysis methods face many challenges when dealing with highly heterogeneous tumor images, such as large differences in image quality and unclear or complex tumor features. Although breakthroughs have been made in image processing with deep learning techniques, there are still limitations in identifying small or irregular tumors. Existing tumor detection models often rely on local feature extraction, neglecting global information and subtle differences in the images, which limits their accuracy and robustness in practical applications. To address these issues, this paper proposes a deep learning model that integrates Feature Pyramid Network (FPN) and Vision Transformer (ViT) within an end-to-end architecture. Firstly, the model extracts rich features at multiple scales through FPN, covering various aspects from cellular structures to tissue layouts. Then, by introducing ViT, the model can effectively process and analyze global features, particularly achieving higher accuracy in recognizing ambiguous or complex tumor patterns. The self-attention mechanism further enhances the model’s focus on critical regions of the image, improving its ability to detect subtle differences. Finally, the design of the end-to-end architecture enhances the overall efficiency and consistency of the model, facilitating global optimization and further improving detection and classification performance. The experimental results show that compared to existing techniques, this model demonstrates higher recognition accuracy on medical image datasets such as TCIA, BraTS, LUNA, and Camelyon17. The accuracy and F1 scores improved by 4.65% to 6.24%. These algorithmic improvements not only enhance the efficiency and accuracy of tumor detection but also provide new pathways for the application of deep learning in medical image analysis.

Non-melanoma skin cancer (NMSC) is among the most prevalent forms of cancer originating in the top layer of the skin, with basal cell carcinoma (BCC) and squamous cell carcinoma (SCC) being its primary categories. While both types are highly treatable, the success of treatment hinges on early diagnosis. Early-stage NMSC detection can be achieved through clinical examination, typically involving visual inspection. An alternative, albeit invasive, method is a skin biopsy. Microwave imaging has gained prominence for non-invasive early detection of various cancers, leveraging distinct dielectric properties of healthy and malignant tissues to discriminate tumors and categorize them as benign or malignant. Recent studies demonstrate the potential of terahertz (THz) spectroscopy for detecting biomarkers by aligning electromagnetic wave frequencies in the low THz range (0.1 to 10 THz) with resonant frequencies of biomolecules, such as proteins. This study proposes an innovative microscale biosensor designed to operate in the THz range for the high-sensitivity and efficient diagnosis of non-melanoma skin cancer. By incorporating meticulously designed metamaterial layers, the sensor's absorption properties can be controlled, a critical aspect for discriminating between normal and NMSC-affected skin. In particular, the interaction between skin and THz waves, influenced by dielectric properties and unique vibrational resonances of molecules within tissue, is crucial for wave propagation and scattering. Extensive numerical studies showcased the suitability of the proposed biosensor for NMSC diagnosis, illustrated through specific case studies. These findings hold the potential to pave the way for further development of non-invasive microwave-imaging-based techniques for detecting NMSC and other types of skin cancer.

In recent years, the realms of computer vision and deep learning have ushered in transformative changes across various domains. Among these, deep learning stands out for its remarkable capacity to handle vast datasets, revolutionizing numerous fields, including the biomedical sector. In particular, its prowess has been harnessed in the realm of brain tumor identification through MRI scans, yielding impressive results. This research project is dedicated to conducting an exhaustive exploration of existing endeavors in the domain of brain tumor identification and classification via MRI scans. This endeavor is poised to be of profound value to researchers looking to leverage their deep learning expertise in the realm of brain tumor detection and categorization. The initial phase involves an overview of prior studies that have employed deep learning for categorising and detecting brain tumors. Subsequently, a meticulous analysis of deep learning studies proposed in research publications spanning (2019 to 2022) is presented in tabular form. The conclusion section comprehensively assesses the merits and demerits inherent in deep neural networks. The insights gleaned from this study promise to equip future researchers with a holistic perspective on current research trends and a nuanced understanding of the effectiveness of diverse deep learning methodologies. It is our fervent belief that this research will significantly advance the understanding of brain tumors and their detection methodologies.

Abstract Background Tumor biomarkers have used widely in clinical oncology in human medicine. Only a few studies have evaluated the clinical utility of tumor biomarkers for veterinary medicine. A test for fibrinogen and fibrin degradation products (DR‐70) has been proposed as an ideal biomarker for tumors in humans. The clinical value of DR‐70 for veterinary medicine however has yet to be determined. Objectives Investigate the diagnostic value of DR‐70 concentrations by comparing them between healthy dogs and dogs with tumors. Animals Two hundred sixty‐three dogs with different types of tumors were included. Sixty healthy dogs also were recruited for comparison. Methods The DR‐70 concentrations were measured in all recruited individuals by ELISA. Clinical conditions were categorized based on histopathology, cytology, ultrasound examination, radiology, clinical findings, and a combination of these tests. Results The median concentration of DR‐70 was 2.130 ± 0.868 μg/mL in dogs with tumors, which was significantly higher than in healthy dogs (1.202 ± 0.610 μg/mL; P

This paper presents a prototype of a portable and modular electrical impedance tomography (EIT) system for breast tumor detection. The proposed system uses MATLAB to generate three-dimensional representations of breast tissue. The modular architecture of the system allows for flexible customization and scalability. It consists of several interconnected modules. Each module can be easily replaced or upgraded, facilitating system maintenance and future enhancements. Testing of the prototype has shown promising results in preliminary screening based on experimental studies. Agar models were used for the experimental stage of this project. The 3D representations provide clinicians with valuable information for accurate diagnosis and treatment planning. Further research and refinement of the system is warranted to validate its performance in future clinical trials.

Medical imaging, also known as radiology, is the field of medicine in which medical professionals recreate various images of parts of the body for diagnostic or treatment purposes. Medical imaging procedures include non-invasive tests that allow doctors to diagnose injuries and diseases without being intrusive TechTarget (n.d.). A number of tools and techniques are used to automate the analysis of medical images acquired with various image processing methods. The brain is one of the largest and most complex organs of the human body and anomaly detection from brain images (i.e., MRI, CT, PET, etc.) is one of the major research areas of medical image analysis. Image processing methods such as filtering and thresholding models, geometry models, graph models, region-based analysis, connected component analysis, machine learning (ML) models, the recent deep learning (DL) models, and various hybrid models are used in brain image analysis. Brain tumors are one of the most common brain diseases with a high mortality rate, and it is difficult to analyze from brain images for the versatility of the shape, location, size, texture, and other characteristics. In this paper, a comprehensive review on brain tumor image analysis is presented with basic ideas of brain tumor, brain imaging, brain image analysis tasks, brain image analysis models, brain tumor image features, performance metrics used for evaluating the models, and some available datasets on brain tumor/medical images. Some challenges of brain tumor analysis are also discussed including suggestions for future research directions. The graphical abstract summarizes the contributions of this paper. [ABSTRACT FROM AUTHOR]

At present, the prediction of brain tumors is performed using Machine Learning (ML) and Deep Learning (DL) algorithms. Although various ML and DL algorithms are adapted to predict brain tumors to some range, some concerns still need enhancement, particularly accuracy, sensitivity, false positive and false negative, to improve the brain tumor prediction system symmetrically. Therefore, this work proposed an Extended Deep Learning Algorithm (EDLA) to measure performance parameters such as accuracy, sensitivity, and false positive and false negative rates. In addition, these iterated measures were analyzed by comparing the EDLA method with the Convolutional Neural Network (CNN) way further using the SPSS tool, and respective graphical illustrations were shown. The results were that the mean performance measures for the proposed EDLA algorithm were calculated, and those measured were accuracy (97.665%), sensitivity (97.939%), false positive (3.012%), and false negative (3.182%) for ten iterations. Whereas in the case of the CNN, the algorithm means accuracy gained was 94.287%, mean sensitivity 95.612%, mean false positive 5.328%, and mean false negative 4.756%. These results show that the proposed EDLA method has outperformed existing algorithms, including CNN, and ensures symmetrically improved parameters. Thus EDLA algorithm introduces novelty concerning its performance and particular activation function. This proposed method will be utilized effectively in brain tumor detection in a precise and accurate manner. This algorithm would apply to brain tumor diagnosis and be involved in various medical diagnoses aftermodification. If the quantity of dataset records is enormous, then themethod's computation power has to be updated. [ABSTRACT FROM AUTHOR]

The integration of photonics and artificial intelligence (AI) has led to the emergence of intelligent photonics, which offers significant advancements in medical imaging. In this paper, a Photonic Crystal Fiber (PCF)-based sensor is presented for tumor detection. The finite element method is used to simulate the proposed sensor. By varying the geometrical parameters of the proposed sensor, an optimized sensor is proposed. Meanwhile, the latest AI techniques used in medical imaging, such as deep learning (DL) and convolutional neural networks (CNN), are also analyzed to improve upon the ability of the sensor. This paper highlights the potential of intelligent photonics in improving efficiency, sensitivity, specificity and accuracy of medical imaging, particularly in the areas of tumor detection and treatment. The results show that DL has an efficiency of 95%, and CNN has shown an accuracy of 98%. Additionally, this paper discusses the challenges and limitations that need to be addressed in order to fully realize the potential of these technologies. This paper demonstrates that the integration of photonics and AI has great potential to revolutionize medical imaging. [ABSTRACT FROM AUTHOR]

Objectives: The prevalence of minimally invasive surgeries has increased the need for tumor detection using thoracoscopic images during lung cancer surgery. We conducted this study to analyze the efficacy of a deep convolutional neural network (DCNN) for tumor detection using recorded thoracoscopic images of pulmonary surfaces. Materials and methods: We collected 644 intraoperative thoracoscopic images of changes in pulmonary appearance from 427 patients with lung cancer between 2012 and 2021. The lesion areas on the thoracoscopic images were detected by bounding boxes using an advanced version of YOLO, a well-known DCNN for object detection. The DCNN model was trained and evaluated by a 15-fold cross-validation scheme. Each predicted bounding box was considered successful detection when it overlapped more than 50% of the lesion areas annotated by board-certified surgeons. Results and conclusions: Precision, recall, and F1-measured values of 91.9%, 90.5%, and 91.1%, respectively, were obtained. The presence of lymphatic vessel invasion was associated with successful detection (p = 0.045). The presence of pathological pleural invasion also showed a tendency toward successful detection (p = 0.081). The proposed DCNN-based algorithm yielded an accuracy of more than 90% tumor detection. These algorithms will help surgeons detect lung cancer displayed on a screen automatically. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI)-enhanced histopathology presents unprecedented opportunities to benefit oncology through interpretable methods that require only one overall label per hematoxylin and eosin (H&E) slide with no tissue-level annotations. We present a structured review of these methods organized by their degree of verifiability and by commonly recurring application areas in oncological characterization. First, we discuss morphological markers (tumor presence/absence, metastases, subtypes, grades) in which AI-identified regions of interest (ROIs) within whole slide images (WSIs) verifiably overlap with pathologist-identified ROIs. Second, we discuss molecular markers (gene expression, molecular subtyping) that are not verified via H&E but rather based on overlap with positive regions on adjacent tissue. Third, we discuss genetic markers (mutations, mutational burden, microsatellite instability, chromosomal instability) that current technologies cannot verify if AI methods spatially resolve specific genetic alterations. Fourth, we discuss the direct prediction of survival to which AI-identified histopathological features quantitatively correlate but are nonetheless not mechanistically verifiable. Finally, we discuss in detail several opportunities and challenges for these one-label-per-slide methods within oncology. Opportunities include reducing the cost of research and clinical care, reducing the workload of clinicians, personalized medicine, and unlocking the full potential of histopathology through new imaging-based biomarkers. Current challenges include explainability and interpretability, validation via adjacent tissue sections, reproducibility, data availability, computational needs, data requirements, domain adaptability, external validation, dataset imbalances, and finally commercialization and clinical potential. Ultimately, the relative ease and minimum upfront cost with which relevant data can be collected in addition to the plethora of available AI methods for outcome-driven analysis will surmount these current limitations and achieve the innumerable opportunities associated with AI-driven histopathology for the benefit of oncology. [ABSTRACT FROM AUTHOR]

There is an emerging need for medical imaging data to provide timely diagnosis. The segmentation of brain tumours from Magnetic Resonance Imaging (MRI) is of great significance for planning treatment. However, mechanising the process with different imaging conditions and accuracy is a significant challenge due to variations in tumour structures. An effective optimisation-driven classifier is developed for brain tumour detection to increase accuracy. The proposed Modified Butterfly Optimization Algorithm (MBOA) is employed for training the Deep Convolutional Neural Network (DCNN) for brain tumour detection. The input MRI image is pre-processed using a Gaussian filter to eliminate noises. The pre-processed output is fed to segmentation, wherein the U-Net model is adapted for generating the segments. The extraction of statistical and texture features is done for tumour region classification. The classification of tumours is done with DCNN, wherein the weights are optimally tuned using the proposed MBOA. The experimental result demonstrates that the developed method achieved better. [ABSTRACT FROM AUTHOR]

Background: Tumor biomarkers have used widely in clinical oncology in human medicine. Only a few studies have evaluated the clinical utility of tumor biomarkers for veterinary medicine. A test for fibrinogen and fibrin degradation products (DR‐70) has been proposed as an ideal biomarker for tumors in humans. The clinical value of DR‐70 for veterinary medicine however has yet to be determined. Objectives: Investigate the diagnostic value of DR‐70 concentrations by comparing them between healthy dogs and dogs with tumors. Animals: Two hundred sixty‐three dogs with different types of tumors were included. Sixty healthy dogs also were recruited for comparison. Methods: The DR‐70 concentrations were measured in all recruited individuals by ELISA. Clinical conditions were categorized based on histopathology, cytology, ultrasound examination, radiology, clinical findings, and a combination of these tests. Results: The median concentration of DR‐70 was 2.130 ± 0.868 μg/mL in dogs with tumors, which was significantly higher than in healthy dogs (1.202 ± 0.610 μg/mL; P <.0001). With a cut‐off of 1.514 μg/mL, the sensitivity and specificity of DR‐70 were 84.03% and 78.33%, respectively. The area under curve was 0.883. The DR‐70 concentration can be an effective tumor biomarker in veterinary medicine. Conclusions and Clinical Importance: Increased DR‐70 concentrations were not affected by tumor type, sex, age, or body weight. However, in dogs with metastatic mast cell tumors and oral malignant melanoma, DR‐70 concentrations were significantly increased. Additional studies, including more dogs with nonneoplastic diseases, are needed to further evaluate the usefulness of DR‐70 as a tumor biomarker. [ABSTRACT FROM AUTHOR]

Digital breast tomosynthesis (DBT) stands out as a highly robust screening technique capable of enhancing the rate at which breast cancer is detected. It also addresses certain limitations that are inherent to mammography. Nonetheless, the process of manually examining numerous DBT slices per case is notably time-intensive. To address this, computer-aided detection (CAD) systems based on deep learning have emerged, aiming to automatically identify breast tumors within DBT images. However, the current CAD systems are hindered by a variety of challenges. These challenges encompass the diversity observed in breast density, as well as the varied shapes, sizes, and locations of breast lesions. To counteract these limitations, we propose a novel method for detecting breast tumors within DBT images. This method relies on a potent dynamic ensemble technique, along with robust individual breast tumor detectors (IBTDs). The proposed dynamic ensemble technique utilizes a deep neural network to select the optimal IBTD for detecting breast tumors, based on the characteristics of the input DBT image. The developed individual breast tumor detectors hinge on resilient deep-learning architectures and inventive data augmentation methods. This study introduces two data augmentation strategies, namely channel replication and channel concatenation. These data augmentation methods are employed to surmount the scarcity of available data and to replicate diverse scenarios encompassing variations in breast density, as well as the shapes, sizes, and locations of breast lesions. This enhances the detection capabilities of each IBTD. The effectiveness of the proposed method is evaluated against two state-of-the-art ensemble techniques, namely non-maximum suppression (NMS) and weighted boxes fusion (WBF), finding that the proposed ensemble method achieves the best results with an F1-score of 84.96% when tested on a publicly accessible DBT dataset. When evaluated across different modalities such as breast mammography, the proposed method consistently attains superior tumor detection outcomes. [ABSTRACT FROM AUTHOR]

Breast cancer is without doubt the leading cancer among women, and it is one of the most damaging illnesses to females that should be periodically checked. Early detection of breast cancer can reduce the mortality caused by this disease by 95%. However, studies mention that up to 25% of tumors are missed by radiologists. In this paper, a tumor detection algorithm in mammogram images is developed by relying on simple calculations that are based on adaptive thresholding and tumor area size. Low complexity calculations will ease the implementation of the algorithm in embedded systems and in real-time detection. The proposed algorithm is used to detect the circular type of tumor and it is developed with a graphical user interface to ease the process of selecting mammogram images and changing settings of threshold values and the size of tumor area. Experimental results show the ability of the algorithm to successfully detect and differentiate circular tumors from normal and fatty breast tissue.