https://milestoneresearch.in/JOURNALS/index.php/TMT/issue/feedMilestone Transactions on Medical Technometrics2025-02-21T04:31:07+00:00Dr. Syed Thouheed Ahmededitor_technometrics@milestoneresearch.inOpen Journal Systems<p><strong><em>Milestone Transactions on Medical Technometrics</em> [ISSN:</strong> <strong>2584-072X</strong>] is a medical journal dedicated towards technological advancements in biomedical sciences within the domain of engineering and technological innovations. Milestone Transactions on Medical Technometrics invites researchers to submit novel and unpublished research and surveys. The journal includes the aspects of biomedical innovations and research using computer science and engineering domains such as artificial intelligence (AI), machine learning (ML), intelligent communication, data processing, human computer interaction (HCI) systems and much more.</p>https://milestoneresearch.in/JOURNALS/index.php/TMT/article/view/161Machine Learning Technique for Brain Tumor Detection: A Comprehensive Review2025-02-03T13:40:03+00:00Kamleshwar Dubeya@gmail.comPradeep Choukseya@gmail.comPraveen Sadotraa@gmail.comMayank Chopraa@gmail.com<div><strong> </strong></div> <p>Detecting</p> <div><span lang="EN-IN"> 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.</span></div>2025-02-03T00:00:00+00:00Copyright (c) 2025 Kamleshwar Dubey, Pradeep Chouksey, Praveen Sadotra, Mayank Choprahttps://milestoneresearch.in/JOURNALS/index.php/TMT/article/view/164Automated Tuberculosis Detection from Chest X-Rays Using a ResNet50 Architecture2025-02-21T04:31:07+00:00Sreelakshmi Induria@gmail.comM Reddi Durgasreea@gmail.comBeeda Sukumara@gmail.comG Ramasubba Reddya@gmail.comY Subba Reddya@gmail.comJ Jagadeswara Reddya@gmail.com<div><span lang="EN-IN">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.</span></div>2025-02-21T00:00:00+00:00Copyright (c) 2025 Sreelakshmi Induri, M Reddi Durgasree, Beeda Sukumar, G Ramasubba Reddy, Y Subba Reddy, J Jagadeswara Reddy