Acute Lymphoblastic Leukemia Blood Cell Image Classification Using Convolutional Neural Network
Abdul Rahim Ahmad, Mohd Suhaimi Sulaiman, Muhammad Abdul Rauf Zulkifli, Zuraidi Saad, Muhammad Khusairi Osman, Nurul Hazwani Abd Halim · Journal of Advanced Research in Applied Sciences and Engineering Technology · 2025
Due to the disease's late discovery, the number of new cases of Acute lymphoblastic leukemia (ALL) is rising along with its high fatality rates. However, the traditional method of diagnosing Acute lymphoblastic leukemia often involves multiple tests and procedures that leads the patient into life-threatening stages while the interpretation of the results of physical examination, blood tests, and imaging studies is sometimes leads to less accuracy and can be subjective and vary between different healthcare professionals. With the advancements of AI and big data analytics, early diagnosis of Acute lymphoblastic leukemia can be used to aid the clinical decisions of physicians and radiologists. In this study, an automated blood sample classification system using CNN classifier, which is Xception, VGG16, AlexNet, SqueezeNet, MobileNetV2 and ResNet-18, united with the Stochastic Gradient Descent (SGD) optimization algorithm has been developed to predict and classify normal WBC and ALL subtypes from microscopic peripheral blood cell image. The evaluation metrics, confusion matrices, average confidence levels, ROC curves, and highest/lowest confidence levels for each class are analysed to assess the efficacy of each classifier. The result from comparing the performance of the classifier found that AlexNet demonstrated the high accuracy, precision, recall, and F1-score, with an accuracy of 98.9%. AlexNet also had high average confidence levels for each ALL subtype, indicating its strong understanding and discrimination of the different subtypes in classifying normal WBC and ALL subtypes.