Computer-Aided Diagnosis System for Classifying Acute Lymphoblastic Leukaemia (ALL) Using Artificial Intelligence Techniques in MATLAB

Abstract

Acute lymphoblastic leukemia (ALL), an aggressive blood cancer, needs to be identified early for the best possible treatment results. In order to achieve extremely accurate ALL categorization, this study suggests a MATLAB-based Computer-Aided Diagnosis (CAD) system that combines a Support Vector Machine (SVM) classifier with a 28-layer tailored Convolutional Neural Network (CNN) with edge, shape, and color filters. The suggested model improves morphological feature extraction and contrast for increased classification precision in the dataset, which consists of 3,256 microscopic blood smear images. Experimental results outperform current approaches in terms of sensitivity, specificity, and accuracy [1]. By reducing diagnostic variability, this AI-driven method improves patient prognosis by enabling early and accurate leukemia identification [2][3].