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Deep learning-powered AI tools offer significant potential to improve COVID-19 lung infection diagnosis. This study proposes a novel AI-based image analysis method for multiclass classification. We analyzed publicly available datasets from Italian Society of Medical and Interventional Radiology (SIRM), Kaggle, and Radiopaedia. However, the relevance, strength, and relationships of static features extracted from these images require further investigation. Bayesian inference approaches have recently emerged as powerful tools for analyzing static features. These approaches can reveal hidden dynamics and relationships between features. Using Analysis of variance (ANOVA) based ranking techniques, we extracted gray level co-occurrence matrix (GLCM) features from images belonging to three classes such as COVID-19, bacterial pneumonia, and normal. To delve deeper into the dynamic behavior and optimize its diagnostic potential, Homogeneity (identified as the most significant feature) was chosen for further analysis using dynamic profiling and optimization methods. This focused investigation aimed to decipher the intricate, non-linear dynamics within GLCM features across all three classes. Our method offers a two-fold benefit. First, it deepens our understanding of the intricate relationships between features extracted from chest X-rays using gray level co-occurrence matrix analysis. Second, it provides a comprehensive examination of these features themselves. This combined analysis sheds light on the hidden dynamics that are crucial for accurate diagnosis and prognosis of various infectious diseases. In addition to the above, we have developed a novel AI-powered imaging analysis method for multiclass classification. This innovative approach has the potential to significantly improve diagnostic accuracy and prognosis of infectious diseases, particularly COVID-19. [ABSTRACT FROM AUTHOR]

Accurate radiogenomic classification of brain tumors is important to improve the standard of diagnosis, prognosis, and treatment planning for patients with glioblastoma. In this study, we propose a novel two-stage MGMT Promoter Methylation Prediction (MGMT-PMP) system that extracts latent features fused with radiomic features predicting the genetic subtype of glioblastoma. A novel fine-tuned deep learning architecture, namely Deep Learning Radiomic Feature Extraction (DLRFE) module, is proposed for latent feature extraction that fuses the quantitative knowledge to the spatial distribution and the size of tumorous structure through radiomic features: (GLCM, HOG, and LBP). The application of the novice rejection algorithm has been found significantly effective in selecting and isolating the negative training instances out of the original dataset. The fused feature vectors are then used for training and testing by k-NN and SVM classifiers. The 2021 RSNA Brain Tumor challenge dataset (BraTS-2021) consists of four structural mpMRIs, viz. fluid-attenuated inversion-recovery, T1-weighted, T1-weighted contrast enhancement, and T2-weighted. We evaluated the classification performance, for the very first time in published form, in terms of measures like accuracy, F1-score, and Matthews correlation coefficient. The Jackknife tenfold cross-validation was used for training and testing BraTS-2021 dataset validation. The highest classification performance is (96.84 ± 0.09)%, (96.08 ± 0.10)%, and (97.44 ± 0.14)% as accuracy, sensitivity, and specificity respectively to detect MGMT methylation status for patients suffering from glioblastoma. Deep learning feature extraction with radiogenomic features, fusing imaging phenotypes and molecular structure, using rejection algorithm has been found to perform outclass capable of detecting MGMT methylation status of glioblastoma patients. The approach relates the genomic variation with radiomic features forming a bridge between two areas of research that may prove useful for clinical treatment planning leading to better outcomes. [ABSTRACT FROM AUTHOR]

Accurate classification of brain tumor subtypes is important for prognosis and treatment. Researchers are developing tools based on static and dynamic feature extraction and applying machine learning and deep learning. However, static feature requires further analysis to compute the relevance, strength, and types of association. Recently Bayesian inference approach gains attraction for deeper analysis of static (hand-crafted) features to unfold hidden dynamics and relationships among features. We computed the gray level co-occurrence (GLCM) features from brain tumor meningioma and pituitary MRIs and then ranked based on entropy methods. The highly ranked Energy feature was chosen as our target variable for further empirical analysis of dynamic profiling and optimization to unfold the nonlinear intrinsic dynamics of GLCM features extracted from brain MRIs. The proposed method further unfolds the dynamics and to detailed analysis of computed features based on GLCM features for better understanding of the hidden dynamics for proper diagnosis and prognosis of tumor types leading to brain stroke. [ABSTRACT FROM AUTHOR]

We present results of Multifractal Detrended Fluctuation Analysis (MF-DFA) of soil radon (222Rn) and thoron (220Rn) time series to examine the scaling and multifractal features. Data consists of 15,692 measurements taken each over forty-minute interval. RTM 1688–2, 222Rn/220Rn electronic device has been used for measurement purposes. Seasonal periodicities have been removed from the data and MF-DFA was employed on de-seasonalized data. Original 222Rn and 220Rn time series have been converted into surrogated and shuffled time series. MF-DFA has also been applied to the surrogated and shuffled series to examine the multifractality nature. Results of the study show that Orig 222 Rn , Surr 222 Rn and Orig 220 Rn , Surr 220 Rn time series are long-range positively correlated and an increase or decrease in either of radionuclide concentrations will be followed by another increase or decrease of concentrations in future measurements. The Shuff 220 Rn time series has an independent or short-range dependent structure called white noise and the shuffled time series exhibit a very weak multifractality. [ABSTRACT FROM AUTHOR]

Prostate Cancer in men has become one of the most diagnosed cancer and also one of the leading causes of death in United States of America. Radiologists cannot detect prostate cancer properly because of complexity in masses. In recent past, many prostate cancer detection techniques were developed but these could not diagnose cancer efficiently. In this research work, robust deep learning convolutional neural network (CNN) is employed, using transfer learning approach. Results are compared with various machine learning strategies (Decision Tree, SVM different kernels, Bayes). Cancer MRI database are used to train GoogleNet model and to train Machine Learning classifiers, various features such as Morphological, Entropy based, Texture, SIFT (Scale Invariant Feature Transform), and Elliptic Fourier Descriptors are extracted. For the purpose of performance evaluation, various performance measures such as specificity, sensitivity, Positive predictive value, negative predictive value, false positive rate and receive operating curve are calculated. The maximum performance was found with CNN model (GoogleNet), using Transfer learning approach. We have obtained reasonably good results with various Machine Learning Classifiers such as Decision Tree, Support Vector Machine RBF kernel and Bayes, however outstanding results were obtained by using deep learning technique. [ABSTRACT FROM AUTHOR]

Particulate matters (PMs) are considered as one of the air pollutants generally associated with poor air quality in both outdoor and indoor environments. The composition, distribution and size of these particles hazardously affect the human health causing cardiovascular health problems, lung dysfunction, respiratory problems, chronic obstructive pulmonary disease and lungs cancer. Classification models developed by analyzing mass concentration time series data of atmospheric particulate matter can be used for the prediction of air quality and for issuing warnings to protect the health of the public. In this study, mass concentration time series data of both outdoor and indoor particulates matters PM2.5 (aerodynamics size up to 2.5 μ) and PM10.0 (aerodynamics size up to 10.0 μ) were acquired using Haz-Dust EPAM-5000 from six different locations of the Muzaffarabad city, Azad Kashmir. The linear and nonlinear approaches were used to extract mass concentration time series features of the indoor and outdoor atmospheric particulates. These features were given as an input to the robust machine learning classifiers. The support vector machine (SVM) kernels, ensemble classifiers, decision tree and K-nearest neighbors (KNN) are used to classify the indoor and outdoor particulate matter time series. The performance was estimated in terms of area under the curve (AUC), accuracy, true negative rate, true positive rate, negative predictive value and positive predictive value. The highest accuracy (95.8%) was obtained using cubic and coarse Gaussian SVM along with the cosine and cubic KNN, while the highest AUC, i.e., 1.00, is obtained using fine Gaussian and cubic SVM as well as with the cubic and weighted KNN. [ABSTRACT FROM AUTHOR]

Epilepsy is a neurological disorder produced due to abnormal excitability of neurons in the brain. The research reveals that brain activity is monitored through electroencephalogram (EEG) of patients suffered from seizure to detect the epileptic seizure. The performance of EEG detection based epilepsy require feature extracting strategies. In this research, we have extracted varying features extracting strategies based on time and frequency domain characteristics, nonlinear, wavelet based entropy and few statistical features. A deeper study was undertaken using novel machine learning classifiers by considering multiple factors. The support vector machine kernels are evaluated based on multiclass kernel and box constraint level. Likewise, for K-nearest neighbors (KNN), we computed the different distance metrics, Neighbor weights and Neighbors. Similarly, the decision trees we tuned the paramours based on maximum splits and split criteria and ensemble classifiers are evaluated based on different ensemble methods and learning rate. For training/testing tenfold Cross validation was employed and performance was evaluated in form of TPR, NPR, PPV, accuracy and AUC. In this research, a deeper analysis approach was performed using diverse features extracting strategies using robust machine learning classifiers with more advanced optimal options. Support Vector Machine linear kernel and KNN with City block distance metric give the overall highest accuracy of 99.5% which was higher than using the default parameters for these classifiers. Moreover, highest separation (AUC = 0.9991, 0.9990) were obtained at different kernel scales using SVM. Additionally, the K-nearest neighbors with inverse squared distance weight give higher performance at different Neighbors. Moreover, to distinguish the postictal heart rate oscillations from epileptic ictal subjects, and highest performance of 100% was obtained using different machine learning classifiers. [ABSTRACT FROM AUTHOR]

The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code: (22UQU4310373DSR40)." now reads: "Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2023R161), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia. The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code: (22UQU4310373DSR62). [Extracted from the article]