DeepPCOS is a cutting-edge project leveraging deep learning and machine learning techniques to assist in the diagnosis and management of Polycystic Ovary Syndrome (PCOS). PCOS is a prevalent hormonal disorder affecting reproductive-age women, often resulting in infertility and various health complications. This project aims to provide healthcare professionals with tools to analyze medical data and offer insights into PCOS diagnosis and treatment.
- 🔍 Automatic Detection: Automatically identifies PCOS indicators in medical images, such as ovarian cysts and related abnormalities.
- 📊 Severity Classification: Analyzes medical imaging data to classify the severity of PCOS, aiding in personalized treatment planning.
- 🔗 Integration: Seamless integration with existing medical record systems to streamline the diagnosis and treatment process.
- 🔄 Feedback-Based Learning: Continuous improvement through feedback loops with healthcare professionals to enhance accuracy and performance.
- 📈 Interactive Visualization: Provides easy-to-understand visualizations for patients and medical experts.
- Improve early detection of PCOS using advanced machine learning techniques.
- Provide a reliable and scalable solution for PCOS severity assessment.
- Aid in research by providing insights from aggregated medical imaging data.
- Programming Language: Python 🐍
- Deep Learning Framework: TensorFlow / PyTorch
- Libraries: Pandas, NumPy, Matplotlib, OpenCV, Scikit-learn
- Model Architecture: Convolutional Neural Networks (CNNs) for image classification
- Deployment: Flask/Django (backend) with integration on platforms like HuggingFace Spaces
- Source: Kaggle dataset on PCOS and other related medical datasets.
- Features:
- 🖼️ Medical imaging data (e.g., ultrasound images)
- 👩 Patient demographics: Age, weight, BMI, etc.
- 🩸 Blood test indicators: Hormonal levels, insulin resistance markers.
- Data Preprocessing:
- ✨ Normalization and standardization of images.
- 🔄 Data augmentation techniques for improving model generalization.
- Data Collection: Collect and preprocess PCOS-related medical images and metadata.
- Feature Extraction: Use convolutional layers to extract significant features from the images.
- Model Training: Train the CNN model for classification and severity prediction.
- Evaluation: Test the model on a separate validation dataset to ensure robustness.
- Deployment: Deploy the trained model for use in real-world medical settings.