I have a curated dataset of abdominal X-ray images that needs a robust deep-learning model capable of classifying key clinical findings. The end goal is a production-ready Python solution that can consistently score above 90 % accuracy on an unseen validation set. You’ll start with any mainstream framework you prefer—TensorFlow, Keras, or PyTorch—and handle the full pipeline: data preparation and augmentation, model architecture selection, training, hyper-parameter tuning, and evaluation. Please keep the code modular and well-commented so I can retrain or fine-tune later as new data comes in. A concise report that explains your decisions, metrics, and suggestions for future improvements will also be appreciated. To help me choose quickly, focus your proposal on your experience with medical imaging or similar computer-vision tasks. If you have previously reached high accuracy on radiology datasets or worked with domain-specific augmentations, let me know. Deliverables: • Python source code and any supporting scripts/notebooks • Trained weights or a clear checkpoint file • README with setup steps and inference instructions • Brief performance report confirming >90 % accuracy on my hold-out set I’m keen to get this moving right away and can provide sample images as soon as we agree on the approach.