Acute Lymphoblastic Leukemia (ALL) Detection Using Deep Learning

Acute Lymphoblastic Leukemia (ALL) is a severe hematological malignancy, primarily impacting youngsters and necessitating prompt, accurate diagnosis for maximum therapeutic results. Conventional diagnostic methods, such manual blood smear examination, tend to be labor-intensive and subjective. In recent years, deep learning (DL) techniques have transformed medical diagnostics, especially in image-based disease identification. This research provides a comprehensive analysis of deep learning applications in the identification of acute lymphoblastic leukemia (ALL), encompassing convolutional neural networks (CNNs) and hybrid models. We emphasize the advantages and drawbacks of different methodologies and address the issues of class imbalance, dataset heterogeneity, and incorporation into clinical workflows. The model was trained and tested using a public dataset and achieved high accuracy of 97.8%. These results showcase deep learning techniques in medical diagnostics inspiring a robust machinery which can be a solution for ALL diagnosis. This work can greatly help healthcare professionals in doing early diagnosis and treatment planning.