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A recent study conducted by researchers at Pomeranian Medical University in Szczecin, Poland, focuses on paragangliomas, which are rare tumors originating from the autonomic nervous system. The study aims to summarize the results of genetic testing for patients with head and neck paraganglioma and create an updated genetic diagnosis algorithm based on previous studies. The research found that certain mutations, such as SDHD and SDHB, are associated with head and neck paragangliomas, and genetic testing for VHL is not necessary without a positive family history. The study also analyzed different treatment algorithms and proposed an updated diagnostic management scheme. [Extracted from the article]

A recent report from the Saveetha School of Engineering in Chennai, India, discusses the development of a deep learning framework using a convolution neural network algorithm for breast cancer detection and classification. Breast cancer is a significant health concern globally, affecting both men and women. The proposed framework aims to improve the efficiency and accuracy of breast cancer diagnosis by automatically analyzing breast images. The research concludes that the deep learning framework, specifically the convolution neural network, is highly reliable compared to existing algorithms. This research has been peer-reviewed and provides valuable insights into the field of cancer detection. [Extracted from the article]

A study conducted by researchers at Prince Sattam bin Abdulaziz University in Saudi Arabia explores the issue of balancing generation and demand in interconnected microgrids (MGs) to address security, power quality, and reliability concerns. The study proposes an optimized fractional order load frequency control (LFC) scheme with cascaded outer and inner control loops. The proposed controller, based on a modified version of the liver cancer optimization algorithm (MLCA), achieves better disturbance rejection compared to traditional LFC methods. The study also considers the contribution of electric vehicles and the high penetration of renewables to test the stability of the proposed LFC technique. The results confirm the superior response and improved performance of the proposed controller. [Extracted from the article]

Researchers from the School of Computer Science and Engineering in Tamil Nadu, India, have developed a multi-model deep learning framework and algorithms for predicting the survival rates of different subtypes of lung cancer using histopathology imagery. The proposed models, LCSCNet and LCSANet, achieved high accuracy rates of 96.55% and 95.85% respectively, outperforming existing models. This research has the potential to enhance treatment management by automating lung cancer diagnosis and survival analysis. For more information, the full article can be accessed through IEEE Access. [Extracted from the article]

Researchers from Kyungpook National University in South Korea have developed a new approach to skin lesion segmentation using a Genetic Algorithm (GA) guided feature selection. The researchers replaced the ResNet block with a custom block in the Modified TMU-Net architecture to improve adaptability to variable input sizes. The dual-pipeline design combines transformers for global representations and convolutional neural networks for local contextual representations. The GA is used to optimize image channel selection, resulting in enhanced segmentation. The proposed model achieved successful outcomes when trained on the ISIC 2017 dataset. [Extracted from the article]

Researchers at the Medical University of Plovdiv in Bulgaria have explored the role of artificial intelligence (AI) algorithms in prostate cancer histopathology. They found that AI algorithms are increasingly being used to identify Gleason patterns in prostate cancer, offering a solution to the variability in grading among pathologists. The implementation of AI has improved efficiency and demonstrated positive outcomes as a supplementary tool for pathologists. While refinements are still needed before full implementation in clinical practice, AI shows promise in enhancing workflows and improving patient outcomes. [Extracted from the article]

A recent study conducted by researchers at VIT Bhopal University in Madhya Pradesh, India, focuses on the use of machine learning algorithms to analyze gene expression data for cancer classification. The study introduces a hybrid feature selection algorithm called kSHAP, which combines with other algorithms to enhance accuracy, robustness, and scalability in cancer classification tasks. The researchers evaluated the performance of six classifiers and found that the Autoencoder consistently outperformed the others. The kSHAP-bSSD algorithm demonstrated superior performance across multiple datasets, achieving an accuracy rate of 99.9% in lung adenocarcinoma. The study concludes that kSHAP-bSSD and kSHAP-ABHC combinations are effective in cancer classification tasks. [Extracted from the article]

Researchers from Graphic Era (Deemed to be University) have conducted a study on lung cancer prediction using machine learning algorithms. The study highlights the increasing cases of lung cancer due to the excessive growth of PM 2.5 in aerosol, with the highest mortality rate among other types of cancer. The researchers aimed to compare twelve potential machine learning algorithms to accurately predict positive cases of lung cancer based on clinical data and symptoms. The study found that the K-Nearest Neighbor Model and Bernoulli Naive Bayes Model were the most significant methods for early lung cancer prediction. [Extracted from the article]

A report from Universiti Teknologi PETRONAS in Malaysia discusses research on colon cancer. The researchers developed an efficient approach for colon cancer segmentation and image synthesis using advanced generative adversarial networks (GANs) guided by the Sine Cosine Algorithm (SCA) for hyperparameter tuning. The method achieved state-of-the-art results, outperforming existing methods, and shows promise in enhancing the accuracy and robustness of colon cancer diagnosis and treatment. This research could lead to better diagnosis and treatment of colon cancer. The full report can be accessed through IEEE Access. [Extracted from the article]

A study conducted by researchers at the University of Health Sciences in Adana, Turkey, has developed a deep machine learning algorithm to diagnose thyroid cancer. The algorithm addresses the challenges of multi-dimensional big data and randomly distributed missing data, which are common in thyroid cancer diagnoses. The algorithm achieved 100% training and 83% testing preciseness for unseen data, and its computational time efficiency was also examined. This research provides valuable insights into the use of machine learning in diagnosing thyroid cancer. [Extracted from the article]

A new study conducted at the University of Kastamonu in Turkey has developed an innovative method for the automated detection of colon cancer using artificial intelligence. The researchers utilized a convolutional neural network (CNN) and the Fishier Mantis Optimizer algorithm to improve the accuracy and efficiency of colon cancer diagnosis. The proposed method was evaluated using a comprehensive dataset of colon cancer images and demonstrated superior performance compared to traditional diagnostic approaches. The integration of deep learning techniques and bio-inspired optimization algorithms holds promise for enhancing early detection and diagnosis of colon cancer. [Extracted from the article]

A new report from the University of Massachusetts discusses the use of large language models (LLMs) like ChatGPT and Bard in clinical decision-making for cancer screening. The study evaluated the accuracy of these models in responding to open-ended and select-all-that-apply prompts based on the American College of Radiology Appropriateness Criteria. The results showed similar performances between ChatGPT and Bard, with ChatGPT slightly more accurate in select-all-that-apply scenarios. Prompt engineering techniques were also used to improve the accuracy of the models' outputs. The study suggests that LLMs have potential in aiding clinical decision-making processes, especially when guided by well-engineered prompts. Further research in diverse clinical situations is needed to better understand the impact of LLMs in radiology. [Extracted from the article]

A recent study conducted by researchers at Shanxi Medical University in China proposes a novel methodology for the early detection of gastric cancer. The study combines the use of the Alexandria Net (Alexnet), Extreme Learning Machine (ELM), and a modified metaheuristic algorithm to enhance the accuracy and efficiency of gastric cancer detection. The researchers applied this methodology to a practical dataset and found that it outperformed several state-of-the-art methods in terms of precision, accuracy, recall, specificity, F1-score, and Area Under the Curve (AUC). The study concludes that the combination of Alexnet and ELM with the modified metaheuristic algorithm has the potential to significantly impact the field of gastric cancer detection. [Extracted from the article]

Researchers from Shandong University in China have developed a new strategy for diagnosing oral cancer using a combination of effective learning and medical imaging. The strategy utilizes Gated Recurrent Unit (GRU) networks optimized by an improved version of the Northern Goshawk Optimization (NGO) algorithm. This approach has several advantages, including the ability to analyze large and complex datasets, high accuracy, and the capacity to detect oral cancer at an early stage. The study demonstrates the efficiency of this approach in accurately diagnosing oral cancer. [Extracted from the article]

A study conducted by researchers from the Chinese Academy of Sciences discusses a new automatic detection method for bladder tumor cells based on color and shape features. Bladder urothelial carcinoma is a common malignant tumor disease in the urinary system, and this study aims to improve the efficiency of its pathological diagnosis. The researchers propose an algorithm that combines color and shape features extracted from microscopic hyperspectral data, achieving a sensitivity of 0.952, specificity of 0.897, and accuracy of 0.920. This method could potentially assist doctors in choosing treatment plans and predicting the prognosis of bladder urothelial cancer. [Extracted from the article]

This article discusses a study that compares seven different algorithms for tracing fibre-like structures in biomedical images. The analysis of these structures can help in understanding processes like angiogenesis and the effects of certain treatments. The study found that no single algorithm outperformed the others in all metrics, but the Trace Ridges algorithm ranked first in terms of total distance error. The algorithm is fully automatic and combines various image-processing techniques to accurately delineate the fibre-like structures. The article also provides information on where to access the code for the Trace Ridges algorithm. Please note that this preprint has not been peer-reviewed. [Extracted from the article]

Researchers from Sohag University have developed a new algorithm, called RBNRO-DE, for gene selection in cancer classification using high-dimensional data. The algorithm was tested on 22 cancer datasets and compared to other meta-heuristic algorithms. The results showed that the RBNRO-DE algorithm achieved optimal convergence and classification accuracy up to 100%, with a feature reduction size of 98%. This research highlights the potential of RNA sequencing as a technique for gene profiling and quantification in cancer research. [Extracted from the article]

A study conducted by researchers at the Nanyang Institute of Technology in Henan, China, proposes a new methodology for the early diagnosis of melanoma skin cancer using dermoscopic images. The method involves scaling and enhancing the quality of the images, extracting features based on the Gray-Level Co-occurrence Matrix (GLCM), and using a newly developed version of the Archimedes Optimization Algorithm (DAOA) to select the most relevant features. The selected features are then classified using a Support Vector Machine (SVM) to distinguish between benign and malignant lesions. The proposed method achieved high values for performance indicators and demonstrated an accuracy of 88%, sensitivity of 96%, specificity of 81%, precision of 97%, and F-measure of 97% for the diagnosis of melanoma. [Extracted from the article]

A recent study conducted by the School of Computer Science at Cairo University focused on the early detection of hepatocellular carcinoma (HCC), a type of liver cancer, in Egyptian patients. The study proposed a model that utilizes machine learning algorithms to examine circulating microRNAs (miRNAs) as diagnostic and predictive markers for HCC. The model consists of three phases: data preprocessing, feature selection using a new algorithm called Binary African Vulture Optimization Algorithm (BAVO), and classification with support vector machine and k-folds cross-validation. The results showed that miRNAs-483-5p, 21, and 155 may serve as potential biomarkers for the early detection and prognosis of HCC. [Extracted from the article]

A recent report from the School of Electrical Engineering in Tamil Nadu, India discusses the use of machine learning and computer vision in cancer classification. The researchers highlight the potential of these technologies in speeding up the diagnosis of cancer and improving treatment planning. The report focuses on nine major cancer types and examines the use of various imaging modalities and machine learning algorithms in their classification. The objective of the study is to provide researchers and medical experts with a comprehensive understanding of the current state of cancer classification using computer vision and machine learning, with the aim of enhancing cancer detection and therapy precision. [Extracted from the article]

A research study conducted at Delhi Technological University in Delhi, India, has developed a new algorithm-based technique for diagnosing breast cancer. The study utilized the Wisconsin Breast Cancer Dataset and employed both SVM and KNN algorithms for data analysis. The proposed hybrid algorithm, called SSA-GWO, achieved an accuracy of 99.42% when applied to the dataset. The research concludes that the hybrid algorithm shows promise for diagnosing breast cancer in challenging situations. [Extracted from the article]

A new report discusses research findings on cancer gene selection using omics data. Researchers at Henan University in Kaifeng, People's Republic of China, have developed a two-stage prior LASSO (TSPLASSO) algorithm for gene selection. The algorithm incorporates prior knowledge of cancer genes and performs sample classification and variable selection simultaneously. Numerical simulations show that TSPLASSO improves the accuracy of variable selection by 5%-400% in various datasets. The TSPLASSO algorithm is considered efficient, stable, and practical for exploring biomedical and health informatics from omics data. [Extracted from the article]

A new study from the Institute of Information Technologies in Moscow, Russia, explores the use of blood protein markers as biomarkers for cancer detection. The researchers propose an approach that involves creating cost-sensitive SVM classifiers based on datasets with a variety of features, including known blood protein markers and new features calculated using entropy and fractal dimensions. The study found that the application of the UMAP algorithm, approximate entropy, and fractal dimensions can be used to construct new features using blood protein markers. The addition of these new features improved the performance of the SVM classifiers in terms of the MacroF1-score metric. [Extracted from the article]

A study conducted at the Affiliated Hospital of Guangdong Medical University in China explores the relationship between gut microbial homeostasis and liver cancer. The researchers propose an eigenvalue-based goodness-of-fit algorithm to evaluate intestinal microorganisms and liver cancer. They also analyze the diagnostic role of lgEr/lgFp on hepatocellular carcinoma and its relationship with AFP. The study concludes that there is a significant difference in the lgEr/lgFp ratio between the high AFP cirrhosis group and the liver cancer group, highlighting the influence of intestinal homeostasis on the development of liver cancer. [Extracted from the article]

A recent study conducted by researchers at the Nanyang Institute of Technology in China has developed a novel method for detecting oral cancer using an optimized version of Convolutional Neural Network (CNN). The researchers incorporated the Seagull Optimization Algorithm and Particle Swarm Optimization Algorithm to optimize the CNN architecture specifically for oral cancer detection. By combining these algorithms, the proposed method enhances the performance and accuracy of the CNN in identifying cancerous regions in oral images. The results of the study showed that the proposed CSOA-based CNN system achieved the highest accuracy rate (96.94%) compared to other methods, indicating its superior performance in oral cancer detection. The researchers concluded that their innovative integration of these algorithms with CNN, along with advanced image processing techniques, provides a reliable and effective solution for early detection of oral cancer. [Extracted from the article]