Android-Based Herpes Disease Detection Application using Image Processing

Abstract

Herpes is a viral infection that causes a skin disease that is widespread throughout the world. Herpes virus is a DNA virus transmitted via infected skin, saliva, and other body fluids. Herpes is characterized by chickenpox-like nodules in one area of the skin, swollen tissue surrounding the nodule, and blister formation on the nodule. Digital image processing that can detect herpes disease is anticipated to reduce physical contact between physicians and patients during skin disease diagnosis. This study's methodology includes collecting data on herpes disease, developing machine-learning models using the CNN algorithm, and deploying the model as an Android application. This study makes use of actual data collected via smartphones, Pocket Cameras, and internet-sourced photographs. The data include 12,645 images of skin affected by herpes and normal skin. Using 100 epochs and the Adadelta optimizer, the accuracy of this study is 85 percent.