Acute Lymphoblastic Leukemia Blood Cell Image Classification System Using Convolutional Neural Network

Leukemia is a cancer of white blood cells that originates in the bone marrow due to dysregulated blood cell production. Manual microscopic diagnosis of bone marrow blood samples is time-consuming, error-prone, and subject to inter-observer variability, as differences in pathologists' expertise may lead to inconsistent diagnoses. Integrating artificial intelligence (AI) can improve diagnostic accuracy and efficiency. This study proposes a deep learning-based method using a convolutional neural network (CNN) to automate the classification of acute lymphoblastic leukemia (ALL) from microscopic images. A dataset of 770 images was collected from HUSM (Hospital Universiti Sains Malaysia). The images underwent preprocessing (resizing, augmentation) and were evaluated using three CNN architectures: AlexNet, VGG-16, and ResNet-18. The best-performing model was selected through hyperparameter tuning, optimizing the optimizer, initial learning rate, mini-batch size, and learning rate schedule. Performance was assessed using accuracy, precision, sensitivity (recall), and F1-score. Finally, the performance metrices for classification such as accuracy, precision, sensitivity and F1-score are used to evaluate the model effectiveness. ResNet-18 achieved the highest performance, with scores of 99.56% accuracy, 99.71% precision, 99.60% sensitivity, and 99.56% F1-score. This model will be integrated into a computer-aided diagnosis (CAD) system to assist hematologists in classifying ALL from microscopic blood samples.