Abstract
Background
Skin cancer is the most common cancer in the United States. Current estimates are that one in five Americans will develop skin cancer in their lifetime. A skin cancer diagnosis is challenging for dermatologists requiring a biopsy from the lesion and histopathological examinations. In this article, we used the HAM10000 dataset to develop a web application that classifies skin cancer lesions.
Method
This article presents a methodological approach that utilizes dermoscopy images from the HAM10000 dataset, a collection of 10015 dermatoscopic images collected over 20 years from two different sites, to improve the diagnosis of pigmented skin lesions. The study design involves image pre-processing, which includes labelling, resizing, and data augmentation techniques to increase the instances of the dataset. Transfer learning, a machine learning technique, was used to create a model architecture that includes EfficientNET-B1, a variant of the baseline model EfficientNET-B0, with a global average pooling 2D layer and a softmax layer with 7 nodes added on top. The results of the study offer a promising method for dermatologists to improve their diagnosis of pigmented skin lesions.
Results
The model performs best in detecting melanocytic nevi lesions with an F1 score of 0.93. The F1 score for Actinic Keratosis, Basal Cell Carcinoma, Benign Keratosis, Dermatofibroma, Melanoma, and Vascular lesions was consecutively 0.63, 0.72, 0.70, 0.54, 0.58, and 0.80.
Conclusions
We classified seven distinct skin lesions in the HAM10000 dataset with an EfficientNet model reaching an accuracy of 84.3%, which provides a promising outlook for further development of more accurate models.
Funder
Arak University of Medical Sciences
Publisher
Public Library of Science (PLoS)
Reference31 articles.
1. Cancer statistics for the year 2020: An overview;J Ferlay;International Journal of Cancer,2021
2. Vital signs: melanoma incidence and mortality trends and projections—United States, 1982–2030;GP Guy;MMWR Morb Mortal Wkly Rep,2015
3. Prevalence of a history of skin cancer in 2007: results of an incidence-based model;RS Stern;Arch Dermatol,2010
4. The epidemiology of UV induced skin cancer;BK Armstrong;Journal of Photochemistry and Photobiology B: Biology,2001
5. Basic histological structure and functions of facial skin;O Arda;Clin Dermatol,2014
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