Machine learning approaches for skin neoplasm diagnosis
Asaduzzaman, Abu Thompson, Christian C. Uddin, Md. Jashim
Asaduzzaman, Abu
Thompson, Christian C.
Uddin, Md. Jashim
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Location
Time Period
Advisors
Original Date
Digitization Date
Issue Date
2024-07-15
Type
Article
Genre
Keywords
Machine learning,Anatomy,Layers,Lesions,Algorithm
Subjects (LCSH)
Citation
Asaduzzaman, A., Thompson, C. C., & Uddin, M. J. (2024). Machine learning approaches for skin neoplasm diagnosis. ACS Omega, 9(30), 32853-32863. https://pubs.acs.org/doi/full/10.1021/acsomega.4c03640
Abstract
Approaches for skin neoplasm diagnosis include physical exam, skin biopsy, lab tests of biopsy samples, and image analyses. These approaches often involve error-prone and time-consuming processes. Recent studies show that machine learning shows promise in effectively classifying skin images into different categories such as melanoma and melanocytic nevi. In this work, we investigate machine learning approaches to enhance the performance of computer-aided diagnosis (CADx) systems to diagnose skin diseases. In the proposed CADx system, generative adversarial network (GAN) discriminator is used to identify (and remove) fake images. Exploratory data analysis (EDA) is applied to normalize the original data set for preventing model overfitting. Synthetic minority oversampling technique (SMOTE) is employed to rectify class imbalances in the original data set. To accurately classify skin images, the following machine learning models are utilized: linear discriminant analysis (LDA), support vector machine (SVM), convolutional neural network (CNN), and an ensemble CNN-SVM. Experimental results using the HAM10000 data set demonstrate the ability of the machine learning models to improve CADx performance in treating skin neoplasm. Initially, the LDA, SVM, CNN, and ensemble CNN-SVM show 49%, 72%, 77%, and 79% accuracy, respectively. After applying GAN (discriminator) and SMOTE, the LDA, SVM, CNN, and ensemble CNN-SVM show 76%, 83%, 87%, and 94% accuracy, respectively. We plan to explore other machine learning models and data sets in our next endeavor.
Table of Contents
Description
Publisher
American Chemical Society
Journal
ACS Omega
Book Title
Series
Digital Collection
Finding Aid URL
Use and Reproduction
Archival Collection
PubMed ID
ISSN
2470-1343