Abstract:
The Skin Disease Identification Project aims to develop an advanced system using
Machine Learning to build a user-friendly prototype of a system that with a simple
picture can identify skin diseases in machine learning. This will be taken from a cross platform mobile application identifying a variety of skin diseases with a wide range of
conditions, including dermatitis, psoriasis, eczema, acne, and various infections.
Diagnosis often requires a time-consuming process of visual examination, medical
history review, and lab tests. The proposed system will utilize a vast dataset of
dermatological approved and annotated images to train deep learning models that can
effectively recognize and classify skin diseases based on color features and decomposed
skin image and its melanin levels. The primary goal of this project is to enhance the
accuracy or efficiency of skin disease diagnosis. By employing state-of-the-art image
recognition and pattern analysis algorithms, the system will be able to rapidly process
skin images and provide instant disease identification. This will serve as the basis for
helping dermatologists with effective and instant diagnosis, therefore, saving time and
reducing the delay in the diagnosis. The system will first identify the features of the
images and then train the model based on it using various algorithms mentioned below
which after a popular vote will determine the best match of the identification of the
project.
