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Conference Paper by alukuntabalakrishna5532 is a document available to read on EtoBox.
This paper presents a multi-modal CNN-driven binary classifier for the automated detection of Acute Lymphoid Leukemia (ALL) from blood smear images, achieving 98.45% accuracy, 84.79% sensitivity, and 100% specificity. The proposed methodology includes CMYK color space transformation for enhanced nuclear contrast, advanced segmentation techniques, and a hybrid feature extraction strategy that combines texture and spatial features with deep learning representations. The system addresses limitations of manual
- Author
- alukuntabalakrishna5532
- Language
- EN