Xray Bodypart Classifier

⚠️ Intended use & limitations

• Intended use: a suggestion/assist signal — surface the likely body part to a human reviewer, ideally as a ranked top-k list behind a confidence threshold.
• NOT for clinical or diagnostic use. It classifies anatomy, not pathology, and must never drive an unsupervised clinical decision.
• Coarse labels with known overlap. Several classes are hierarchical / overlapping (HEAD↔SKULL, KUB↔ABDOMEN, SPINE↔LUMBAR/CERVICAL/DORSAL_SPINE, EXTREMITY↔ARM/LEG/FOREARM). This caps top-1 (a KUB image read as ABDOMEN is "wrong" but practically correct), which is why top-5 (0.94) is the more meaningful number than top-1 (0.70).
• Weak on rare / overlapping classes (see per-class table) — FINGER, HEEL, KUB, ARM, HIP have few samples and/or collapse into larger classes. Use confidence thresholding in production.
• Trained on adult-population radiographs from routine practice; behaviour on paediatric, exotic, or heavily-processed images is unverified.

Performance

Held-out validation: 7,354 images, 33 classes.

Per-class recall (validation)

The high-volume, visually distinct anatomies are strong (0.77–0.98); the weak rows are the overlapping/hierarchical and low-sample classes. Merging those into a cleaner ~15–18-class taxonomy is the obvious path to a substantially higher-accuracy v2.

Model details

• Architecture: convnext_tiny (timm), ImageNet-pretrained, fine-tuned.
• Input: RGB image, resize shorter edge to 224, center-crop 224×224, scale to [0,1], normalize with ImageNet mean [0.485, 0.456, 0.406] / std [0.229, 0.224, 0.225], layout NCHW. (No horizontal-flip augmentation — it would corrupt left/right laterality.)
• ONNX I/O: input images [N,3,224,224] float32 → output probs [N,33] (softmax). Class order is classes.txt.
• Files: model.onnx (FP32) · best.pt (PyTorch state dict, for fine-tuning).

Usage

pip install -r requirements.txt
python inference_example.py path/to/xray.jpg

import numpy as np, onnxruntime as ort
from PIL import Image

classes = [c.strip() for c in open("classes.txt")]
MEAN, STD = np.float32([0.485,0.456,0.406]), np.float32([0.229,0.224,0.225])

img = Image.open("xray.jpg").convert("RGB")
s = 224 / min(img.size); img = img.resize((round(img.size[0]*s), round(img.size[1]*s)))
w, h = img.size; img = img.crop(((w-224)//2, (h-224)//2, (w-224)//2+224, (h-224)//2+224))
x = ((np.asarray(img, np.float32)/255 - MEAN)/STD).transpose(2,0,1)[None]

probs = ort.InferenceSession("model.onnx").run(["probs"], {"images": x})[0][0]
top = probs.argsort()[::-1][:5]
print([(classes[i], round(float(probs[i]), 3)) for i in top])

Training data

~37k de-identified plain-radiograph frames from routine clinical practice, one representative frame per study/series, labelled from the body_part_examined DICOM tag (with the confirmed procedure as a fallback) and normalized through an anatomy lexicon. Manual uploads, multi-body-part studies, and conflicting-label images were excluded; classes were balanced (cap 2,000/class). The training dataset is not released. The dataset owner has confirmed the rights to publish this derived model.

Author

Created by Istiak Hassan Emon — GitHub @emon5122.

If you use this model, please credit:

Istiak Hassan Emon, "X-Ray Body-Part Classifier (ConvNeXt-Tiny)", 2026.
https://huggingface.co/emon5122/xray-bodypart-classifier

License

apache-2.0 (matches the ConvNeXt backbone). © 2026 Istiak Hassan Emon. The model is provided as-is, with no warranty, and not for clinical use.