Omid-Nejati/MedViTV2
MedViTV2: Medical Image Classification with KAN-Integrated Transformers and Dilated Neighborhood Attention (Applied Soft Computing 2025)
This project helps medical professionals and researchers accurately classify medical images, even those with real-world imperfections from various sources. It takes medical images as input and provides classifications like 'Pneumonia' or 'Breast Cancer' with improved reliability. Doctors, radiologists, and medical imaging researchers who need to interpret diverse diagnostic scans would find this tool beneficial.
Use this if you need a highly accurate and efficient way to classify medical images for diagnosis or research, especially when dealing with data that may be noisy or inconsistent.
Not ideal if your primary need is for object detection or segmentation within medical images, as this tool focuses on overall image classification.
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Jupyter Notebook
License
MIT
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Last pushed
Mar 10, 2026
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