Machine Learning Technique for Brain Tumor Detection: A Comprehensive Review

Kamleshwar Dubey, Pradeep Chouksey, Praveen Sadotra, Mayank Chopra — Mon, 03 Feb 2025 00:00:00 +0000

Detecting

diseases in the brain at early stages or proper diagnosis and treatment of tumor in the brain are perhaps the biggest challenges facing medical sciences. New advancement in Machine Learning has developed novel methods for augmenting accuracy and effectiveness of detecting brain tumors based on image obtained through MRI scans. This review goes in-depth analysis of how detection and classification is currently improving, highlighting such techniques involving SVM, CNNs, ResNet, and Xception. Key spatial and textural feature extraction techniques, like PCA, HOG, and GLCM, have also been considered in order to improve the classification performance. Ensemble approaches, such as Random Forest, have been taken into consideration for handling the complexity of the dataset. The study evaluates the benefits, limitations, and clinical applicability of these methods in view of the challenges posed by diverse datasets, high computational demands, and generalization issues. Further research and development are required to make the machine-learning diagnostic capabilities of brain-related conditions more reliable and practical.

Automated Tuberculosis Detection from Chest X-Rays Using a ResNet50 Architecture

Fri, 21 Feb 2025 00:00:00 +0000

TB (tuberculosis) is still a major worldwide health concern, and in order to control its spread and enhance patient outcomes, precise and prompt diagnostic methods are required. This study introduces a deep-learning methodology employing a ResNet50 architecture for the reliable and precise classification of TB in CXR images. The model utilizes a robust CNN (Convolutional Neural Network) to extract essential features, enhanced by image pre-processing techniques to optimize image quality and overall performance. The ResNet50 classifier shows great performance metrics, achieving an accuracy of 99.82%, a 99.82% accuracy rate and an AUC of 99.87%. These results highlight the potential of ResNet50 as a valuable TB detection tool for healthcare professionals, particularly in resource-constrained environments, enabling faster diagnosis and treatment.

Milestone Transactions on Medical Technometrics

Machine Learning Technique for Brain Tumor Detection: A Comprehensive Review

Automated Tuberculosis Detection from Chest X-Rays Using a ResNet50 Architecture