Detecting Acute Lymphoblastic Leukemia in Blood Smear Images using CNN and SVM

Acute Lymphoblastic Leukemia (ALL) is a common and aggressive subtype of leukemia that predominantly affects children. Accurate and timely diagnosis of ALL is critical for successful treatment, but it is hindered by the limitations of manual examination of peripheral blood smear images, which are prone to human error and inefficiency. This study proposes an improved diagnostic approach by integrating the EfficientNet architecture with a Support Vector Machine (SVM) classifier to enhance classification accuracy and address the performance inconsistencies of standalone EfficientNet models. Additionally, a novel CNN-based model with a reduced number of parameters is developed and evaluated. A dataset comprising 3.256 peripheral blood smear images across four classes (benign, early, pre and pro) was used for training and testing. The EfficientNet-SVM models achieved a peak accuracy of 97.35% using the EfficientNet-B3 architecture, surpassing previous studies. The improved CNN model achieved the highest accuracy of 99.18% while reducing parameters by 59.5% compared to the best prior models, with a negligible accuracy decrease of only 0.67%. These findings highlight the potential of combining EfficientNet with SVM and the efficiency of the improved CNN model for automated ALL detection, paving the way for more reliable, cost-effective, and scalable diagnostic tools.