Analyzing the effectiveness of transfer learning models for leukemia detection in blood cells

In recent years, transfer learning has gained significant attention as a feasible and efficient deep learning approach for a variety of medical image analysis tasks, particularly in the area of disease detection and segmentation. This study investigates the effectiveness of various transfer learning models—ResNet50, MobileNet, InceptionV3, DenseNet121, EfficientNetB4, and a proposed model—for blood cell classification. In recent years, transfer learning has gained significant attention as a feasible and efficient deep learning approach for a variety of medical image analysis tasks, particularly in the area of disease detection and segmentation. By leveraging pre-trained networks, we aimed to enhance the accuracy and efficiency of detecting leukemia in blood smear images. Each model was fine-tuned on a comprehensive dataset consisting of normal and leukemic blood cells. The proposed model demonstrated exceptional performance, achieving an accuracy of 99.43%, significantly surpassing the other architectures evaluated. This improvement highlights the potential of transfer learning in medical imaging, particularly in automating and streamlining the diagnostic process for hematological disorders. Future work will focus on optimizing these models further and exploring their applicability in real-world clinical settings.