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Manual inspection of peripheral blood smears remains the standard approach, but it is resource\u2010intensive and subject to observer variability, underscoring the need for automated, consistent, and interpretable diagnostic tools. This work proposes a dual\u2010stage deep learning framework combining transfer learning and a custom CNN model\u2014DAC\u2010Net\u2014for the classification of ALL from microscopic blood smear images. The first stage leverages pretrained CNNs (VGG19, ResNet50, ResNet101) for feature extraction, followed by feature refinement using ANOVA, recursive feature elimination (RFE), and random forest importance scores. These features are classified using multiple traditional machine learning algorithms including SVM, kNN, random forest, and Na\u00efve Bayes. The second pipeline features the DAC\u2010Net, an attention\u2010integrated CNN trained end\u2010to\u2010end to automatically learn discriminative patterns directly from image data. Incorporating spatial attention layers and dense connections, the DAC\u2010Net emphasizes morphologically relevant regions associated with leukemia. Extensive experiments on the C\u2010NMC dataset demonstrated that while both pipelines performed well, the DAC\u2010Net achieved higher sensitivity and F1\u2010scores. The integration of Grad\u2010CAM and related explainability tools adds transparency to model decisions, enhancing its practical value as a decision\u2010support tool in ALL diagnosis.<\/jats:p>","DOI":"10.1155\/acis\/8886728","type":"journal-article","created":{"date-parts":[[2025,10,30]],"date-time":"2025-10-30T11:37:49Z","timestamp":1761824269000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Deep Learning\u2013Based Dual\u2010Pipeline Framework for Acute Lymphoblastic Leukemia Classification With Explainable AI Integration"],"prefix":"10.1155","volume":"2025","author":[{"given":"Aziz","family":"Makandar","sequence":"first","affiliation":[]},{"given":"Arfa","family":"Bhandari","sequence":"additional","affiliation":[]},{"given":"Benita","family":"Veronica","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4054-3339","authenticated-orcid":false,"given":"Ashvini","family":"Alashetty","sequence":"additional","affiliation":[]},{"given":"Jyothi","family":"A. 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