| --- |
| license: apache-2.0 |
| library_name: onnx |
| pipeline_tag: image-classification |
| tags: |
| - medical |
| - radiology |
| - x-ray |
| - radiograph |
| - anatomy |
| - body-part |
| - image-classification |
| - onnx |
| metrics: |
| - accuracy |
| --- |
| |
| # X-Ray Body-Part Classifier (ConvNeXt-Tiny, ONNX) |
|
|
| A CPU-friendly **body-part / anatomy classifier for plain radiographs (X-ray)**. Given a single |
| rendered X-ray frame it predicts the imaged anatomy across **33 classes** (CHEST, KNEE, LUMBAR_SPINE, |
| ABDOMEN, …). Exported to **ONNX** with a built-in softmax, so the output is a ready-to-use probability |
| distribution and it runs anywhere with `onnxruntime` — no GPU required. |
| |
| It was built to fill the "vision gap" in a radiology workflow: suggesting the likely anatomy when the |
| text order / DICOM tags are missing, opaque, or mislabelled. **It is a decision-support suggestion |
| model, not a diagnostic device.** |
| |
| ## ⚠️ 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. |
|
|
| | Metric | Score | |
| |---|---| |
| | Top-1 accuracy | **0.704** | |
| | Top-5 accuracy | **0.940** | |
|
|
| ### Per-class recall (validation) |
|
|
| | Class | Recall | n | | Class | Recall | n | |
| |---|---:|---:|---|---|---:|---:| |
| | SHOULDER | 0.98 | 355 | | SPINE | 0.60 | 272 | |
| | KNEE | 0.97 | 400 | | NECK | 0.60 | 400 | |
| | ABDOMEN | 0.89 | 400 | | LEG | 0.60 | 45 | |
| | CERVICAL_SPINE | 0.84 | 376 | | WRIST | 0.58 | 202 | |
| | CHEST | 0.82 | 400 | | UPPER_EXTREMITY | 0.57 | 400 | |
| | FOOT | 0.80 | 400 | | PELVIS | 0.57 | 400 | |
| | LUMBAR_SPINE | 0.80 | 400 | | LOWER_EXTREMITY | 0.55 | 400 | |
| | PNS | 0.79 | 199 | | FOREARM | 0.53 | 95 | |
| | ANKLE | 0.78 | 292 | | HEAD | 0.50 | 400 | |
| | ELBOW | 0.77 | 237 | | SI_JOINT | 0.38 | 8 | |
| | SKULL | 0.77 | 400 | | FEMUR | 0.29 | 34 | |
| | DORSAL_SPINE | 0.75 | 101 | | EXTREMITY | 0.27 | 56 | |
| | HAND | 0.73 | 390 | | NASOPHARYNX | 0.24 | 62 | |
| | TEMPORAL_BONE | 0.71 | 17 | | HIP | 0.16 | 32 | |
| | TIBIA | 0.67 | 57 | | ARM | 0.04 | 24 | |
| | | | | | KUB | 0.00 | 71 | |
| | | | | | FINGER | 0.00 | 15 | |
| | | | | | HEEL | 0.00 | 14 | |
| |
| 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 |
|
|
| ```bash |
| pip install -r requirements.txt |
| python inference_example.py path/to/xray.jpg |
| ``` |
|
|
| ```python |
| 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](https://github.com/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.** |
|
|
