Automated Leukemia Classification with a Deep Neural Network and Computer Vision
Vaishnav Patil · International Journal for Research in Applied Science and Engineering Technology · 2025
Abstract: Theuncheckedgrowthofaberrantwhitebloodcells is the hallmark of leukemia, a serious hematological cancer that starts in the bone marrow and bloodstream and causes bleeding, anemia, and weakened immunity. Early and a precise leukemia diagnosisisnecessarytoenhancepatientsurvivalratesand start treatment procedures on time. In this work, a lightweight MobileNetV2 convolutional neural network (CNN) architectureis used to analyze microscopic blood smear images and presentan automated leukemia classification system. To enhance model performance, advanced preprocessing techniques such as LAB color space segmentation, KMeans clustering, and targeted data augmentation were employed to normalize imaging variabilities and highlight leukemic features. The system was trained and evaluated on a curated dataset comprising 3,242 images from 89 patients, encompassing both benign and malignant cases. Compared to conventional manual examination,thesystemachievessuperiorclassificationaccuracy, precision,andrecall,providingascalableandefficientdiagnostic tool.Notably,MobileNetV2’slightweightarchitectureguarantees quick inference with no processing overhead, which makes it ideal for real-time clinical application, especially in settings with limited resources. By significantly reducing diagnostic time and minimizing human error, it demonstrates the transformative potential of deep learning and computer vision in hematological diagnostics. Future work will focus on expanding multi-class classification capabilities for different leukemia subtypes and integrating explainableAItechniquestoenhanceclinicalinterpretabilityand trustworthiness.