Abstract: Blood cancer detection and classification play a crucial role in early diagnosis and treatment planning. This study proposes a Convolutional Neural Network (CNN)-based approach for the automated detection and classification of blood cancer using Peripheral Blood Smear (PBS) images. The model classifies images into benign and malignant (ALL) categories, further distinguishing between its subtypes: Early Pre-B, Pre-B, and Pro-B. The system is integrated into a web-based application for real-time image analysis. Experimental results demonstrate the effectiveness of CNNs in achieving high classification accuracy, aiding in automated and reliable leukemia diagnosis.
Keywords: Blood Cancer, Acute Lymphoblastic Leukemia, Convolutional Neural Networks, Peripheral Blood Smear, Deep Learning
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