Eshaan Adyanthaya

Presentation link: https://www.canva.com/design/DAGfSqilyEQ/TGKMg1NKRRjOKrWQGuH7Ug/edit?utm_content=DAGfSqilyEQ&utm_campaign=designshare&utm_medium=link2&utm_source=sharebutton
Objective
Deep ChemoCare aims to solve the problem of delayed skin cancer detection by providing an AI-powered tool for early diagnosis. Many individuals lack easy access to dermatologists, leading to late-stage diagnoses and lower survival rates. The target audience includes individuals concerned about skin health, high-risk patients, and those seeking dermatological consultation. The app also facilitates online appointments and hospital navigation, making expert care more accessible.

Concept and Approach
The app utilizes deep learning and computer vision to analyze user-uploaded images of skin conditions. It classifies skin cancer types and stages using a trained AI model. Integrated features include a directory of top dermatologists, an appointment booking system, and hospital navigation. This comprehensive approach ensures that users receive both preliminary diagnosis and direct access to expert medical care.
Impact
By enabling early detection, Deep ChemoCare can significantly improve survival rates and reduce the need for unnecessary hospital visits. It enhances accessibility by connecting users with top dermatologists and providing personalized precautions for better disease management. The app also saves time through online appointment booking and hospital navigation, improving the overall healthcare experience.

Feasibility
The project requires a large dataset of skin cancer images for AI model training, a mobile app for user interaction, and a database of dermatologists and hospitals. Implementation steps include training deep learning models, developing the mobile application, and integrating APIs for hospital listings, booking systems, and navigation services.

Tech Stack
The app is developed using TensorFlow, PyTorch, and OpenCV for image analysis. The mobile application is built in Java for Android, with Firebase used for real-time database management. Google Maps API handles hospital navigation, while Firebase Cloud Messaging enables notifications. Cloudinary is used for cloud storage, and the AI model employs CNN architectures such as ResNet50, EfficientNet, and InceptionV3, with plans to integrate Vision Transformers in the future.

Sustainability
To ensure long-term growth, the app will incorporate real-time updates, AI model improvements through user feedback, and integration with wearable health devices and telemedicine. Future expansions will include detecting other dermatological diseases while maintaining strong data security measures for patient privacy.

Differentiation
Deep ChemoCare stands out from similar solutions due to its advanced AI integration, combining custom CNN models and pretrained architectures for high accuracy. It offers end-to-end dermatology support, from detection to direct connection with doctors. Personalized care recommendations enhance disease management, while real-time updates and future expansions into wearable devices and telemedicine set it apart. The project’s continuous learning approach ensures sustained effectiveness and user trust.