{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T11:52:25Z","timestamp":1791201145339,"version":"4.2.0"},"reference-count":61,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2026,10,4]],"date-time":"2026-10-04T00:00:00Z","timestamp":1791072000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Background: Accurate 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. Methods: We 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 \u201cproxy-patient\u201d clusters. Consequently, we implemented rigorous subject-level grouped 5-fold cross-validation and Hold-Out protocols alongside standard image-level evaluations. Results: Under 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. Conclusions: A 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.<\/jats:p>","DOI":"10.3390\/s26196290","type":"journal-article","created":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T10:57:46Z","timestamp":1791197866000},"page":"6290","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An Artificial Intelligence Framework for Detection of Leukemia with Comparison Between Segmented and Non-Segmented Images"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6798-9341","authenticated-orcid":false,"given":"Rula","family":"Abdallat","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering, Faculty of Engineering, The Hashemite University, P.O. Box 330127, Zarqa 13133, Jordan"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"},{"role":"project-administration","vocabulary":"credit"}]},{"given":"Osama","family":"Al-Bataineh","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Faculty of Engineering, The Hashemite University, P.O. Box 330127, Zarqa 13133, Jordan"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5417-0043","authenticated-orcid":false,"given":"Ali Mohammad","family":"Alqudah","sequence":"additional","affiliation":[{"name":"Independent Researcher, Winnipeg, MB R2M 3R1, Canada"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"software","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]},{"given":"Ausilah","family":"Alfraihat","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Faculty of Engineering, The Hashemite University, P.O. 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