Medical imaging, diagnosis, and healthcare AI applications. This repository contains deep learning projects focused on radiology, medical image analysis, and clinical AI developed by AdamAI-Systems.
A transfer learning model that classifies chest X-ray images as Normal or Pneumonia, complete with an interactive Gradio web interface for real-time diagnosis.
- Tech Stack: Python, TensorFlow / Keras, MobileNetV2, Gradio, HuggingFace Datasets.
- Key Features: Transfer learning with MobileNetV2 backbone, automated dataset download, interactive visual diagnosis interface.
A multi-task deep learning model trained on the RSNA Pneumonia Detection Challenge dataset that simultaneously classifies pneumonia presence and localizes infection regions via bounding boxes.
- Tech Stack: Python, PyTorch, pydicom, kagglehub, scikit-learn.
- Key Features: DICOM image reading and preprocessing, custom CNN with classification + grid-based detection heads, NMS-based box decoding, ROC-AUC and IoU evaluation.
Each project runs inside a Jupyter Notebook (designed for Google Colab). Navigate to the project directory and follow the instructions in its README.md:
- Chest X-Ray Classifier: ai-radiologist-gradio/README.md
- RSNA Multi-Task Detector: rsna-pneumonia-cls-det/README.md
Note: Model weights (
.h5,.pth) and datasets are excluded from this repository via.gitignore. Run the notebooks to download data and train the models.
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