We were able to develop an advanced medical image classification system using machine learning, completed in just one week! The system efficiently classifies vital organs including heart, brain, breast, and limbs images.
Our custom CNN model is optimized for organ recognition with robust generalization capabilities. The architecture emphasizes reliable classification across diverse medical imaging scenarios, ensuring consistent performance on previously unseen images.
Implemented comprehensive data preparation protocols including precise image annotation, intelligent resizing algorithms, and advanced normalization techniques. This methodical approach significantly improved our model's learning efficiency and accuracy.
Created a streamlined Streamlit platform enabling medical professionals to obtain instant classification results. The system provides clear confidence scores and organ identification, making complex ML outputs accessible for clinical use.
Achieved outstanding accuracy with thorough validation using industry-standard metrics (Confidence score & Prediction entropy). The model demonstrates exceptional reliability across diverse medical imaging scenarios.
This project not only enhanced our technical expertise in biomedical AI applications but also showcased the potential of LLM-assisted development in accelerating complex medical solutions. The experience strengthened our capabilities in both deep learning implementation and efficient project management under time constraints.
- Tensorflow
- Tkinter & Keras
- Pydicom
- Nibabel
- PIL
- OpenCv
Ai.Model.mp4
Here is a link for the dataset used in training the model: https://drive.google.com/file/d/1ONFwkFTxkGCnTX5IfnGnE1rg9vz55csv/view?usp=sharing
- Mostafa Hany
- Moustafa Ali
- Mostafa Hazem
- Muhammad Nasser
This project is licensed under the MIT License - see the LICENSE for details.
Email: mostafahany748@gmail.com
LinkedIn: www.linkedin.com/in/moustafaali1