An Artificial Intelligence Framework for Detection of Leukemia with Comparison Between Segmented and Non-Segmented Images.

backgroundAccurate classification of Acute Lymphoblastic Leukemia (ALL) subtypes from peripheral blood smear (PBS) images using deep learning may support automated image-based analysis of this blood malignancy.

methodsWe developed a custom lightweight Convolutional Neural Network (CNN) to classify white blood cells into four subtypes: Benign, Early Pre-B ALL, Pre-B ALL, and Pro-B ALL. We assessed performance using accuracy, ROC curves, and explainability techniques, including upsampled feature map activations, occlusion sensitivity, and SHAP-style explanation overlays, comparing original (Non-segmented) and HSV-Segmented images. To mitigate patient-level data leakage without explicit patient identifiers, we further used unsupervised K-means clustering of HSV color histograms to group 3256 images into 89 "proxy-patient" clusters. Consequently, we implemented rigorous subject-level grouped 5-fold cross-validation and Hold-Out protocols alongside standard image-level evaluations.

resultsUnder standard image-level evaluation, the model achieved 98.8% accuracy on non-segmented images and 91.3% on segmented images. Crucially, this performance advantage was maintained under strict subject-level cross-validation (97.9% vs. 89.7%), confirming the robustness of the findings against data leakage. Explainability visualizations demonstrated that HSV segmentation discarded vital contextual morphological cues, increasing confusion between closely related subtypes.

conclusionsA lightweight CNN can accurately classify ALL subtypes directly from raw PBS images. The evaluated HSV segmentation degraded performance by removing contextual information. This study underscores the necessity of subject-level evaluation in medical imaging and indicates that mandatory preprocessing should be empirically justified rather than assumed beneficial.