Comparative Analysis of GLCM Features for ALL Detection using Convolutional Neural Networks
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Abstract
Acute Lymphocytic Leukemia (ALL) is the most prevalent childhood cancer. Early diagnosis of ALL is essential, since timely and appropriate therapy significantly improves survival rates. ALL entails abnormal white blood cell (WBC) proliferation that weakens the immune system. Despite numerous studies on ALL identification using microscopic blood smear images, challenges remain due to the structural complexity of blood cells, image noise, intensity inhomogeneity, and cell overlap. Deep learning techniques, particularly CNNs, have shown high efficacy in medical image analysis. However, some studies employ GLCM features that lack an empirical basis for their selection. The study aims systematically evaluate nine distinct GLCM features to provide evidence-based reference for their use in ALL detection. Foundational CNN architecture was used as a classifier to establish a performance baseline with raw color images. Then we compared the baseline to models enhanced with each of the nine GLCM features using the public C-NMC 2019 dataset. The results revealed that GLCM Dissimilarity was the most effective feature, yielding an accuracy of 94.37% and an F1-score of 0.9437. The GLCM Dissimilarity significantly outperformed other GLCM features, followed by GLCM Entropy (93.84%), GLCM Mean (93.57%), GLCM Contrast (93.55%), GLCM Standard Deviation (93.13%), and GLCM ASM (90.98%). A model using only color images achieves an accuracy of 90.53%. It is important to note that although several GLCM features improved performance, among the nine features evaluated, those not listed among the top performers achieved lower accuracy than the color image baseline, highlighting the importance of careful feature selection. These results indicate that specific GLCM features, particularly GLCM Dissimilarity, can substantially enhance CNN-based ALL classification, underscoring its potential as a robust image descriptor for diagnostic applications.
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