Skin disease have a serious impact on people life and health therefore, it is necessary to develop digitize methods in order to increase the accuracy of diagnosis for multi-type skin disease. The application will detect the following ?ACTINIC KERATOSES AND INTRAEPITHELIAL CARCINOMA/BOWEN?S,
Skin epidemic detection using Machine learning
Skin disease have a serious impact on people life and health therefore, it is necessary to develop digitize methods in order to increase the accuracy of diagnosis for multi-type skin disease. The application will detect the following “ACTINIC KERATOSES AND INTRAEPITHELIAL CARCINOMA/BOWEN’S, BASAL CELL CARCINOMA, BENIGN KERATOSIS-LIKE LISION,
DERMATOFIBROMA” diseases in real time and suggest medicine.The application will use Convolutional Neural Network(CNN) Algorithm to detect disease.Dermatological disorders are one of the most widespread diseases in the world.Despite being common its diagnosis is extremely difficult for Instance simple disease like acne If left untreated can leave lifelong scars. Another Problem Is waiting for your checkup after taking an appointment from Doctor.
Application is easy to adapt and provide easiness to user,save the cost and time of user also provide easiness for Doctor(Dermatologist)
Project will be started by collecting data on various diseases and after that filtered data will be chosen to create a Dataset for Model Training. The Model then undergo training and testing Phase for image classification using Convolutional Algorithm(CNN). Then the trained model will be integrated in Android Application using Tensor Flow Lite. On the Output the App will be suggest a Medicine for precautions.
The app will detect a disease with the help of dataset which is stored in the backend with the help of algorithm..the app also take the details of user and save it into the database which help to give the precautions.
Application is easy to adapt and provide easiness to user,save the cost and time of user also provide easiness for Doctor(Dermatologist)
| Item Name | Type | No. of Units | Per Unit Cost (in Rs) | Total (in Rs) |
|---|---|---|---|---|
| Graphic Card NVIDIA(GTX 1070) | Equipment | 1 | 52000 | 52000 |
| Total in (Rs) | 52000 |
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