This project aims to detect eye cancer using deep learning techniques. It leverages convolutional neural networks (CNNs) to classify images of the eye and identify cancerous regions.
- High-Resolution Imaging: Captures detailed eye images using advanced technologies.
- AI and Machine Learning: Utilizes deep learning for accurate cancer detection.
- Automated Analysis: Provides immediate diagnostic reports.
- Early Detection: Identifies cancer at early, treatable stages.
- EHR Integration: Connects with electronic health records for seamless data management.
- User-Friendly Interface: Easy for healthcare professionals to navigate.
- Telemedicine Compatibility: Supports remote consultations and diagnoses.
- Routine Exams: Screens for eye cancer during regular check-ups.
- Specialized Clinics: Monitors and assesses cancer progression in known cases.
- Telemedicine: Facilitates remote diagnosis and expert consultations.
- Early Detection: Increases chances of successful treatment.
- Accuracy and Efficiency: Reduces misdiagnosis with advanced analysis.
- Time-Saving: Automates image review for quicker results.
- Accessibility: Provides expert care in remote areas.
- Cost-Effective: Lowers healthcare costs through early intervention.
- Improved Outcomes: Enhances patient care and treatment success.
- EHR Integration: Streamlines patient record management.
Soumodip Das