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Articles

A hybrid explainable deep learning framework for blood cancer classification using CNN-based feature embeddings and random forest decision models

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By Zulfikar Ali Ansari, Hemlata Pant, M. N. V. Kiranbabu, Sanjeet Kumar
| Nature Verified
Abstract The precision and early detection of subtypes of acute lymphoblastic leukaemia (ALL) in peripheral blood smear images are crucial for efficient clinical practice. Traditional deep learning methods tend to be challenging in terms of model interpretation and are often reliant on large-scale annotated datasets, which compromises their practicality in diagnostic applications.
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