Image processing and machine learning-based methods for the diagnosis of skin diseases

Most people who get sick around the world have some kind of skin condition. Despite its frequency, it is very challenging to identify and requires substantial domain experience. This study explains how to identify various types of these disorders. We use a two-stage strategy that efficiently integra...

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Bibliographic Details
Main Authors: Prakash, R. Vijaya, Gantala, Vaishnavi, Ananthula, Rushikesh, Banda, Hemantha
Format: Conference Proceeding
Language:English
Subjects:
Algorithms
Deep learning
Feature extraction
Image processing
Machine learning
Medical imaging
Online Access:Get full text
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Summary:Most people who get sick around the world have some kind of skin condition. Despite its frequency, it is very challenging to identify and requires substantial domain experience. This study explains how to identify various types of these disorders. We use a two-stage strategy that efficiently integrates image processing and deep learning based on molecular diagnostic histological features to reliably detect the disease. During the initial stage of the process, the image of the skin condition is processed in a variety of different ways before “feature extraction” is performed. In the second stage of this process, Machine Learning (ML) algorithms are used to diagnose diseases based on histological characteristics discovered through skin examination. These characteristics can be found by analysing the skin. After being trained and evaluated, the system could correctly name up to 95% of the six diseases.
ISSN:0094-243X
1551-7616
DOI:10.1063/5.0195958