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DermaSense is a final year project built with Python Flask, TensorFlow/Keras, and SQLite for students who want a machine learning based healthcare web application. This final year project allows users to upload skin images, run CNN inference, view predicted disease labels with confidence scores, and maintain private prediction history. The system also supports disease information, precautions, medicine suggestions, optional Grad-CAM overlays, printable reports, and user profile management. The admin console of this final year project includes user management, prediction logs, disease catalog management, precautions, medicines, model upload, model activation, monitoring, and maintenance tools. With support for .keras and .h5 models, DermNet-style class labels, and a demo model generator, DermaSense is suitable for a final year major project in machine learning, Flask, TensorFlow, image processing, or healthcare AI.
Demo User
demo_userUserPass123/auth/loginAdministrator
adminAdminPass123/auth/admin/login.keras and .h5 uploadspixel255 or unitDATABASE_URL override for another databasemodel.h5 workflow.keras demo model generation using create_model.pydermnet_class_labels.json support for correct model output mappingscripts/import_dermnet_catalog.pyh5pycd "path/to/Skin Disease Detection using machine learning and tensorflow"python -m venv .venv.venv\Scripts\activatesource .venv/bin/activatepip install -r requirements.txtpython seed.pypython create_model.pypython scripts/import_dermnet_catalog.pypython run.pyhttp://127.0.0.1:5000/model.h5dermnet_class_labels.jsonDemo User
demo_userUserPass123/auth/loginAdministrator
adminAdminPass123/auth/admin/login