hannahjan06/X-ray-Slab-B-Kaggle
A deep learning ensemble model built for multi-label chest X-ray classification in the Grand X-Ray Slam Division B Kaggle competition. The system integrates three pretrained CNN architectures, data augmentation, class-aware training, and staged fine-tuning to achieve competitive performance on thoracic abnormality detection.
This project helps medical professionals like radiologists or diagnostic technicians quickly identify multiple potential abnormalities from chest X-ray images. You input a chest X-ray, and the system outputs a list of possible conditions present, such as pneumonia, atelectasis, or cardiomegaly. It's designed for someone who needs automated assistance in screening or prioritizing X-ray images for further review.
Use this if you need an automated system to classify chest X-rays for multiple thoracic conditions.
Not ideal if you need a certified diagnostic tool for clinical use, as this is a competition-grade model.
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Nov 22, 2025
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