Acute Lymphoblastic Leukemia Detection and Classification | Data

Hamim Reza

Developed a deep learning approach for diagnosing Acute Lymphoblastic Leukemia (ALL) using peripheral blood smear images. Utilizing a convolutional neural network (CNN), the model achieved 99% training accuracy and 97.09% validation accuracy, effectively distinguishing between ALL subtypes and benign hematogone cases. This solution enhances diagnostic accuracy and efficiency, supporting critical treatment decisions.
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Posted Oct 10, 2024

Developed a deep learning model for diagnosing Acute Lymphoblastic Leukemia (ALL) using peripheral blood smear images, achieving 99% training accuracy.

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