Rathore, Saurabh Pratap SinghAli, GumaChamoli, SakshiLotus, RayappanKumar, YogendraSikarwar, Shailendra Singh2025-12-182025-12-182025-05-09Rathore, S. P. S., Guma, A., Chamoli, S., Lotus, R., Kumar, Y., & Sikarwar, S. S. (2025, March). Machine Learning for Medical Image Feature Extraction. In 2025 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), 3: 1-6.https://dir.muni.ac.ug/handle/20.500.12260/840This scoping review of 266 studies (Jan 2022–Dec 2025) evaluates how integrating Blockchain and quantum machine learning (QML) enhances security, privacy, and reliability in smart water management systems—improving data logging, access control, anomaly detection, predictive maintenance, and optimized distribution. It supports SDG 6 (clean water and sanitation), SDG 9 (industry, innovation, infrastructure), and SDG 11 (sustainable communities) by promoting resilient, efficient water infrastructure. Although largely conceptual, its emphasis on scalable, privacy-preserving hybrid designs aligns with Uganda’s National Development Plan IV goals for industrialization, infrastructure enhancement, and sustainable water resource expansion—contributing to economic transformation and improved public services.Feature extraction from medical images is crucial for harnessing the vast information they contain, aiding in diagnosis, treatment planning, and disease monitoring. Traditional feature extraction methods often struggle to capture the complex patterns and subtle variations in medical images. Recently, machine learning techniques have become powerful tools for automatically extracting discriminative features, enabling more accurate and efficient analysis. This paper provides a comprehensive review of advanced machine learning approaches for medical image feature extraction. It covers various methods, including deep learning architectures, convolutional neural networks (CNNs), and feature learning techniques, highlighting their applications across different medical imaging modalities such as MRI, CT, and X-ray. Our CNN model achieves an average classification accuracy of approximately 94%, outperforming the pneumonia detection accuracy of KNN (91%) and SVM (92%).enSupport vector machinesPneumoniaAccuracyTuberculosisNearest neighbor methodsFeature extractionPlanningConvolutional neural networksX-ray imagingMedical diagnostic imagingOther