Journal Article

Boosting the performance of pretrained CNN architecture on dermoscopic pigmented skin lesion classification

Nugroho, Hanung Adi, Ardiyanto, Igi, Nugroho, Erwin Setyo

2023

Overview

Background Pigmented skin lesions (PSLs) pose medical and esthetic challenges for those affected. PSLs can cause skin cancers, particularly melanoma, which can be life‐threatening. Detecting and treating melanoma early can reduce mortality rates. Dermoscopic imaging offers a noninvasive and cost‐effective technique for examining PSLs. However, the lack of standardized colors, image capture settings, and artifacts makes accurate analysis challenging. Computer‐aided diagnosis (CAD) using deep learning models, such as convolutional neural networks (CNNs), has shown promise by automatically extracting features from medical images. Nevertheless, enhancing the CNN models' performance remains challenging, notably concerning sensitivity. Materials and methods In this study, we aim to enhance the classification performance of selected pretrained CNNs. We use the 2019 ISIC dataset, which presents eight disease classes. To achieve this goal, two methods are applied: resolution of the dataset imbalance challenge through augmentation and optimization of the training hyperparameters via Bayesian tuning. Results The performance improvement was observed for all tested pretrained CNNs. The Inception‐V3 model achieved the best performance compared to similar results, with an accuracy of 96.40% and an AUC of 0.98. Conclusion According to the study, classification performance was significantly enhanced by augmentation and Bayesian hyperparameter tuning.

• Publisher: John Wiley & Sons, Inc,John Wiley and Sons Inc
• Subject: Artificial neural networks / augmentation / Bayes Theorem / Bayesian analysis / Bayesian tuning / Classification / convolutional neural network / Datasets / Deep learning / Diagnosis, Computer-Assisted - methods / Humans / hyper‐parameter optimization / Image enhancement / Lesions / Machine learning / Mathematical models / Medical imaging / Melanoma / Melanoma - pathology / Neural networks / Neural Networks, Computer / Optimization / Original / Pigmentation Disorders / pigmented skin lesion / Skin cancer / Skin diseases / Skin lesions / Skin Neoplasms - pathology / Tuning