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    Area of Science:

    • Medical Imaging
    • Computational Biology
    • Machine Learning in Healthcare

    Background:

    • Microscopic analysis of blood cells is crucial for early diagnosis of hematological disorders like leukemia.
    • Traditional deep Convolutional Neural Networks (CNNs) often overfit small medical image datasets, limiting their diagnostic accuracy.
    • Existing models struggle with the challenge of classifying Acute Lymphoblastic Leukemia (ALL) and Acute Myelogenous Leukemia (AML) on limited data.

    Purpose of the Study:

    • To propose a novel and effective deep learning model for the classification and detection of ALL and AML.
    • To address the overfitting issue commonly encountered with small medical image datasets.
    • To enhance classification performance and computational efficiency in leukemia detection.

    Main Methods:

    • Development of a novel Orthogonal SoftMax Layer (OSL)-based Acute Leukemia detection model.
    • Integration of OSL with ResNet18 for deep feature extraction and classification, enforcing orthogonality in weight vectors.
    • Inclusion of additional dropout and ReLU layers to improve network speed and performance.

    Main Results:

    • The proposed OSL-ResNet18 model demonstrated superior performance in classifying ALL and AML on benchmark datasets (ALLIDB1, ALLIDB2, C_NMC_2019, ASH).
    • The model effectively overcomes the overfitting problem associated with small medical image datasets.
    • Experimental results show significant improvements over existing comparative models in leukemia detection accuracy and efficiency.

    Conclusions:

    • The OSL-ResNet18 model offers a robust and efficient solution for early leukemia diagnosis, particularly in resource-limited scenarios with small datasets.
    • Orthogonal SoftMax Layer integration significantly enhances feature discrimination and computational efficiency.
    • This approach holds promise for improving diagnostic capabilities in hematological disorders through advanced deep learning techniques.