Automated Acute Lymphoblastic Leukemia Detection Using Convolutional Neural Networks.

Abstract

This study investigates the performance of four widely used Convolutional Neural Networks (CNNs) MobileNetV2, InceptionV3, VGG16 and ResNet50 for disease classification tasks, leveraging transfer learning techniques. The analysis focuses on evaluating and comparing the accuracy of these models during both training and testing phases. MobileNetV2 demonstrated slightly lower performance, achieving 95.26% in training and 95.00%, InceptionV3, although effective, showed the lowest accuracy among the models, with training and test accuracies of 93.43% and 93.00%, VGG16 closely followed, with training and test accuracies of 98.78% and 98.00% and in testing ResNet50 emerged as the top-performing model, achieving the highest training accuracy of 99.24% and test accuracy of 99.00% respectively. Reflecting its efficiency but limited capacity in comparison to the top models. respectively. These findings highlight the critical role of model selection in achieving high-performance disease classification and emphasize the suitability of ResNet50 and VGG16 for such tasks. Furthermore, the study underscores the potential of transfer learning to enhance the efficiency of CNNs in medical imaging applications, while also identifying opportunities for further optimization and fine-tuning of these architectures to improve their performance in specific use cases.