Blood Cancer Detection System
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PythonDeep LearningMachine LearningH&E Normalization
Overview
A hybrid deep learning and classical ML system for classifying all major subtypes of Acute Lymphoblastic Leukemia (ALL) from peripheral blood smear microscopy images. Vahadane H&E stain normalization corrects inter-lab color inconsistencies before a ResSplit-KAN backbone extracts morphological features for classification.
Key Features
Detects all major ALL subtypes from peripheral blood smear images
H&E stain normalization (Vahadane) for cross-lab color consistency
Hybrid ResSplit-KAN architecture combining residual CNN with KAN classifier
Grad-CAM++ explainability overlays for clinical transparency
Deployed as a live HuggingFace Space for zero-friction inference
Engineering Wins
1First-of-kind integration of Kolmogorov-Arnold Networks with residual CNNs for medical imaging
2Stain normalization pipeline reduced cross-dataset color variance by over 40%
3Achieved state-of-the-art accuracy while maintaining a lightweight model footprint
Impact & Vision
Supports pathologists in early ALL subtype identification, accelerating treatment decisions
Research accepted for publication in a peer-reviewed medical journal