I have a collection of X-ray studies and I need a robust deep-learning model that can look at each image and instantly tell me which predefined category it belongs to (e.g., chest PA vs. chest lateral, cervical spine, hand, etc.). The job is strictly about classifying the type of X-ray, not diagnosing any pathology. Here is what I already have and what I expect from you: • A curated folder structure with several thousand labelled PNG and DICOM files that you can download from my secure server. • A preference for Python with either PyTorch or TensorFlow/Keras—use whichever framework you feel will achieve the best accuracy and fastest inference on a modern GPU. • Clean, reproducible code (Jupyter notebook or script) plus a short README that explains environment setup, training commands, and how to run inference on a single file or a batch. • A trained model file and a simple inference function/CLI that returns the predicted class and confidence score. Acceptance criteria 1. Top-1 accuracy ≥ 95 % on my held-out validation set. 2. No patient-identifiable metadata must be written to disk or logged. 3. The entire project must run inside Docker or a clearly documented virtual environment so I can reproduce the results on my end. If you have prior experience with medical imaging (DICOM handling, pydicom, MONAI, fastai medical add-ons, etc.) that will be a big plus. I’m ready to hand over the dataset and answer any domain questions as soon as we agree on the timeline.