Automated Cancer Detection in Human Blood Samples of Microscopic Images Using Machine Learning Techniques for Enhanced Diagnosis and Classification - Sushilkumar N. Holambe; Dhiraj Sanjay Hawale (International Journal of Computer Sciences and Engineering, 2025)
Cancer remains one of the leading causes of mortality worldwide, with early detection being critical for improving patient survival rates. Manual microscopic examination of blood samples is time-consuming, subjective, and requires significant expertise, which often leads to diagnostic delays and misinterpretations. In this study, we propose an automated cancer detection framework using microscopic blood smear images analyzed with machine learning techniques in MATLAB. The proposed methodology consists of three major phases: preprocessing and enhancement of blood images, feature extraction using morphological and texture-based descriptors, and classification using machine learning models. The results demonstrate that the system can effectively differentiate cancerous blood samples from normal samples with high accuracy, precision, sensitivity, and specificity. Comparative analysis with existing approaches highlights the superior performance of our system, particularly in terms of robustness and diagnostic reliability. This research contributes to the growing body of computer-aided diagnosis systems and provides a foundation for future improvements in automated cancer diagnostics.