Research Works
Title
Analysis of Automated Skin Disease Classification Exploiting Different Machine Learning Techniques
Abstract
One of the most serious and prevalent diseases in the world is skin disease. Due to the intricacy of humans’ tone and texture and the visible proximity impact of the disorders, it can be quite difficult to pinpoint the exact type of condition at times. Because of this, it’s crucial to recognize skin illnesses at the earliest indication of them. Therefore, it is essential to have a system that can identify skin conditions regardless of these restrictions. With their major advancements, artificial intelligence, computer vision, and machine learning are now crucial to the area of medical research, particularly when it comes to the analysis of medical pictures. In this research, we introduced an automated system based on machine learning for classifying three distinct forms of skin illnesses, including melanoma, basal cell carcinoma, and eczema. For this, we have used different machine learning algorithms like Support Vector Machine, Naive Bayes, Logistic Regression and deep learning models like CNN, LSTM, Bi-LSTM, Inception V3, VGG-16, and Xception. We achieved impressive accuracy of above 90% with every model on a bespoke dataset containing 9665 photos, and the maximum accuracy was 99.33% with the pre-trained deep learning model Xception.
Authors
Tanvir Rahman Anik, Purnendu Talukder , Ishmam Faruki , Ifti Sam Ibn Rahman , Emam Hossain
Novelty and research contributions
- We performed a detailed statistical analysis to assess the feasibility of training supervised algorithms using image-based features.
- This study introduces an interpretive approach for leveraging machine learning to accurately detect early-stage skin diseases with optimal accuracy
- Various machine learning techniques were utilized, including SVM, Logistic Regression, and Naive Bayes, alongside deep learning models like CNN, LSTM, and Bi-LSTM, as well as pre-trained models such as Inception V3, VGG-16, and Xception.