<![CDATA[. Microscopic analysis of peripheral blood smears remains a critical and complex step in leukemia diagnosis, which could greatly benefit from automation using deep learning. In this paper, we compare three different deep learning models for automated classification of leukemia cells: a simple CNN, a ResNet, and a hybrid vision transformer. The Kaggle leukemia image dataset, which includes 15,135 blood smear images, was used. The blood smear images were preprocessed using denoising, normalization, upscaling, and upscaling. Training was performed on high-performance GPUs and evaluated on multiple complex metrics such as F-score, precision, recall, and accuracy. The expected outcomes include identifying the most robust and accurate deep learning model for leukemia classification, providing insights into the strengths and weaknesses of different leukemia subtypes, and demonstrating strategies and the effectiveness of image distortion handling. The results showed that ViT Hybrid models outperformed CNN and ResNet, achieving 89% of accuracy, 88% of precision, 90% of recall, and 89% of F-score.This suggests that hybrid structures hold great promise for improving computer-aided diagnosis in hematology. These findings are expected to contribute significantly to the field of medical image analysis, offering an accurate and scalable diagnostic tool with immediate clinical application.]]>
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