Pingulkar, S., Divekar, D. and Tiwary, A., 2023, November. Deep Learning for Skin Disease Diagnosis with End-to-End Data Security. In 2023 Second International Conference on Informatics (ICI) (pp. 1-6). IEEE.
The scope of this project is to develop a comprehensive skin lesion disease detection system that can accurately diagnose skin diseases with the help of CNN, supporting clinical decision-making by providing pathologists with the highest probability diagnoses. The system will use image processing techniques to analyse skin images and provide pathologists with an accurate diagnosis in a timely manner. The system will be able to differentiate between various types of skin lesions, including cancerous and non-cancerous lesions.
The system will also enable pathologists to generate medical reports based on the diagnosis, which will include all relevant information about the patient's condition. It will also include a database to store patient data and medical reports.
Data security and confidentiality are paramount in the healthcare industry, and the system will employ advanced security measures to ensure the secure transfer of medical data. The system will include a secure method for medical data transfer, where encrypted diagnostic reports will be mailed to the doctors and the password for the encrypted report will be sent to the doctors' phone, ensuring that the data remains secure and confidential.
The project will involve developing a user-friendly interface for pathologists to navigate, allowing them a streamlined and efficient way to diagnose skin diseases and generate medical reports. The system will be designed to be scalable and adaptable, allowing for future updates and additions to the system to incorporate new features and functions as needed.
The scope of the project is limited to the development of the skin lesion disease detection system for a hospital and does not extend to the treatment or management of patient records/medical history. The system will be a tool to aid pathologists in diagnosing skin diseases but will not replace the expertise and judgment of medical professionals.
• HTML5
• TailwindCSS
• JavaScript
• Python
• Flask
• Firebase
• Twilio : Communication API
• Figma for UI design
• Canva for logo
• Pathologist Registration: The system should allow pathologists to register for an account to access the system.
• Upload Image: The system should allow pathologists to upload an image of a skin lesion to the system.
• Process Image: The system should use a CNN model to process the uploaded image and detect any skin lesions.
• Diagnosis: The system should provide a diagnosis based on the skin lesion detection result.
• Medical Report Generation: The system should generate a medical report for the user based on the diagnosis.
• Report Customization: The system should allow pathologists to customize the format of medical reports based on their requirements.
• Store Medical Reports: The system should store the medical reports in a Firebase database.
• Encrypted Reports: The system should generate password-protected diagnostic reports that can only be accessed by authorized healthcare professionals with the right credentials.
• Secure Communication: The system should use secure communication protocols to send the password to authorized healthcare professionals’ mobile devices, including SMS, email or push notifications.
• Clone the repository
• Run the command pip install -r requirements.txt
• Open your terminal and run the python file: python app.py
• A link will appear after running this file.
• Click on this link and use DermaAssist.
| Sr No. | Name | git-profile | |
|---|---|---|---|
| 1. | Aryaman Tiwary | [email protected] | Aryaman0809 |
| 2. | Diti Divekar | [email protected] | DITI2209 |
| 3. | Shriya Pingulkar | [email protected] | shriya02-coder |