| --- |
| license: cc-by-4.0 |
| datasets: |
| - InfoBayAI/xray_clinical_reports_without_findings_medical_nlp |
| - InfoBayAI/xray_clinical_reports_with_findings_medical_nlp |
| language: |
| - en |
| metrics: |
| - accuracy |
| - confusion_matrix |
| base_model: |
| - microsoft/resnet-50 |
| library_name: transformers |
| tags: |
| - computer-vision |
| - medical-imaging |
| - multiclass-classification |
| - diagnostic-imaging |
| - anatomy-classification |
| - xray |
| - pytorch |
| pipeline_tag: image-classification |
| --- |
| |
| **Model Description** |
|
|
| MedXray-ResNet50-BodyPart-Classifier is a deep learning–based medical imaging model designed for automated X-ray anatomy classification using a fine-tuned ResNet-50 architecture. |
|
|
| The model was trained on a curated Medical X-Ray imaging dataset provided by [InfoBay.AI](https://huggingface.co/datasets/InfoBayAI/xray_clinical_reports_with_findings_medical_nlp), containing X-ray scans from several anatomical body regions including chest, skull, spine, pelvis, joints, hands, and foot images. Using transfer learning and fine-tuning techniques, the pretrained ResNet-50 backbone was adapted for medical radiology classification tasks, enabling efficient anatomical X-ray recognition and healthcare-focused computer vision analysis. |
|
|
| This project demonstrates how fine-tuning ImageNet-pretrained computer vision models can be effectively leveraged for healthcare AI, radiology automation, medical image understanding, and diagnostic support systems. |
|
|
|
|
|  |
|
|
| --- |
| **Medical X-ray Anatomy Classification** |
|
|
| The model classifies radiographic scans into the following body regions: |
|
|
| Chest X-ray |
| Skull X-ray |
| Spine X-ray |
| Pelvis X-ray |
| Knee and Joint X-ray |
| Hand X-ray |
| Foot X-ray |
| |
| The system is designed for medical imaging research, radiology AI experimentation, healthcare computer vision workflows, and automated dataset structuring applications. |
|
|
| --- |
|
|
| **X-ray Classification Pipeline** |
|
|
| The complete deep learning workflow used for training is as follows: |
|
|
| Raw X-ray Images → Image Preprocessing → Image Normalization → Dataset Labeling → ResNet50 Fine-Tuning → Multi-Class Classification |
| |
| Data Processing Steps |
| Medical X-ray image ingestion |
| Grayscale to 3-channel RGB conversion |
| Tensor transformation using PyTorch |
| Pixel normalization using mean/std normalization |
| Multi-class anatomical labeling |
| Fine-tuning of pretrained ResNet50 CNN backbone |
| |
| --- |
|
|
|
|
| **Deep Learning Architecture** |
| |
| Architecture: ResNet50 |
| Framework: PyTorch |
| Backbone: ImageNet-pretrained ResNet50 |
| Training Strategy: Transfer Learning + Fine-Tuning |
| Task Type: Multi-Class Image Classification |
| Domain: Medical Imaging / Radiology AI |
| |
| The model uses a fine-tuned ResNet50 convolutional neural network where the final fully connected classification layer was modified to support seven anatomical X-ray categories. |
|
|
|
|
| --- |
|
|
| **Key Features** |
|
|
| Automated X-ray body-part classification |
| Fine-tuned ResNet50 medical imaging model |
| Radiology-focused computer vision pipeline |
| Multi-class anatomical recognition |
| Transfer learning for healthcare AI |
| PyTorch-based medical AI workflow |
| Medical image preprocessing and normalization |
| Deep learning–based radiographic analysis |
| |
| --- |
|
|
| **Dataset Split** |
|
|
| Training Set: 70% |
| Validation Set: 15% |
| Test Set: 15% |
| Split Strategy: Random sampling |
| Number of Classes: 7 |
| |
| --- |
|
|
| **Training Hyperparameters** |
|
|
| Number of Epochs: 5 |
| Batch Size: 16 |
| Learning Rate: 0.0001 |
| Optimizer: Adam |
| Loss Function: Cross-Entropy Loss |
| Input Image Size: 224 × 224 |
| Device Support: CPU / GPU (CUDA) |
| |
| --- |
|
|
| **Model Performance** |
|
|
| The fine-tuned ResNet50 model achieved strong performance on validation and unseen test datasets for anatomical X-ray classification tasks. |
|
|
| Before fine-tuning, the ImageNet-pretrained baseline model achieved only: |
|
|
| Validation Accuracy: 12.2% |
| Test Accuracy: 10.1% |
| |
| After transfer learning and fine-tuning on the multi-region X-ray anatomy dataset, the model achieved: |
|
|
| Validation Accuracy: 97.0% |
| Test Accuracy: 94.0% |
| |
| Performance may vary depending on: |
|
|
| image quality |
| dataset diversity |
| class balance |
| preprocessing consistency |
| domain distribution |
| |
| --- |
| **Classification Labels** |
|
|
| | Class ID | Label | |
| | -------- | ------ | |
| | 0 | Chest | |
| | 1 | Skull | |
| | 2 | Spine | |
| | 3 | Pelvis | |
| | 4 | Joints | |
| | 5 | Hands | |
| | 6 | Foot | |
|
|
| ___ |
| |
| **Usage** |
| |
| **Install Dependencies** |
| |
| ```bash |
| pip install torch torchvision pillow numpy |
| ``` |
| |
| **Load Trained Model** |
| |
| ```python |
| import torch |
| import torch.nn as nn |
| from torchvision import models, transforms |
| from huggingface_hub import hf_hub_download |
| from PIL import Image |
| import json |
| |
| # ============================== |
| # DEVICE |
| # ============================== |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
| |
| # ============================== |
| # DOWNLOAD MODEL FILES |
| # ============================== |
| model_path = hf_hub_download( |
| repo_id="InfoBayAI/ResNet50-Xray-Anatomy-Classifier", |
| filename="xray_resnet50.pth" |
| ) |
| |
| labels_path = hf_hub_download( |
| repo_id="InfoBayAI/ResNet50-Xray-Anatomy-Classifier", |
| filename="labels.json" |
| ) |
| |
| # ============================== |
| # LOAD LABELS |
| # ============================== |
| with open(labels_path, "r") as f: |
| idx_to_class = json.load(f) |
| |
| # ============================== |
| # CREATE MODEL |
| # ============================== |
| model = models.resnet50(pretrained=False) |
| |
| model.fc = nn.Linear( |
| model.fc.in_features, |
| len(idx_to_class) |
| ) |
| |
| # ============================== |
| # LOAD TRAINED WEIGHTS |
| # ============================== |
| model.load_state_dict( |
| torch.load(model_path, map_location=device) |
| ) |
| |
| model.to(device) |
| model.eval() |
| |
| print("Model loaded successfully!") |
| |
| # ============================== |
| # IMAGE TRANSFORM |
| # ============================== |
| transform = transforms.Compose([ |
| transforms.Resize((224, 224)), |
| transforms.Grayscale(num_output_channels=3), |
| transforms.ToTensor(), |
| transforms.Normalize( |
| [0.5, 0.5, 0.5], |
| [0.5, 0.5, 0.5] |
| ) |
| ]) |
| |
| # ============================== |
| # PREDICTION FUNCTION |
| # ============================== |
| def predict_xray(image_path): |
| |
| img = Image.open(image_path).convert("RGB") |
| img = transform(img).unsqueeze(0).to(device) |
| |
| with torch.no_grad(): |
| outputs = model(img) |
| |
| probs = torch.softmax(outputs, dim=1) |
| |
| confidence, pred = torch.max(probs, 1) |
| |
| predicted_label = idx_to_class[str(pred.item())] |
| |
| print(f"Prediction: {predicted_label}") |
| print(f"Confidence: {confidence.item()*100:.2f}%") |
| |
| # ============================== |
| # EXAMPLE |
| # ============================== |
| predict_xray("test_xray.png") |
| ``` |
| |
| |
| --- |
| |
| ### Considerations |
| |
| This model is trained on a X-Ray image dataset of InfoBay.AI and is intended for research and evaluation purposes only. |
| |
| For access to the full dataset or enterprise licensing inquiries, please contact [InfoBay.AI](https://infobay.ai/). |
| |
| Ph: +91 8303174762 |
| Email: datareq@infobay.ai |
