Classification of Skin Lesions Using Weighted Majority Voting Ensemble Deep Learning

Abstract

The conventional dermatology practice of performing noninvasive screening tests to detect skin diseases is a source of escapable diagnostic inaccuracies. Literature suggests that automated diagnosis is essential for improving diagnostic accuracies in medical fields such as dermatology, mammography, and colonography. Classification is an essential component of an assisted automation process that is rapidly gaining attention in the discipline of artificial intelligence for successful diagnosis, treatment, and recovery of patients. However, classifying skin lesions into multiple classes is challenging for most machine learning algorithms, especially for extremely imbalanced training datasets. This study proposes a novel ensemble deep learning algorithm based on the residual network with the next dimension and the dual path network with confidence preservation to improve the classification performance of skin lesions. The distributed computing paradigm was applied in the proposed algorithm to speed up the inference process by a factor of 0.25 for a faster classification of skin lesions. The algorithm was experimentally compared with 16 deep learning and 12 ensemble deep learning algorithms to establish its discriminating prowess. The experimental comparison was based on dermoscopic images congregated from the publicly available international skin imaging collaboration databases. We propitiously recorded up to 82.52% average sensitivity, 99.00% average specificity, 98.54% average balanced accuracy, and 92.84% multiclass accuracy without prior segmentation of skin lesions to outstrip numerous state-of-the-art deep learning algorithms investigated.

SkinLiTE: Lightweight Supervised Contrastive Learning Model for Enhanced Skin Lesion Detection and Disease Typification in Dermoscopic Images

“…Several studies have been conducted significant research in the realm of skin cancer detection and classification, particularly focusing on the challenge of imbalanced datasets in dermatology [89,[95][96][97][98][99][100][101][102][103][104]. These studies collectively advanced the field of medical image classification by addressing the critical issue of imbalanced datasets, each contributing novel techniques and frameworks to improve classification accuracy and efficiency in dermatological and other medical imaging applications.…”

“…This innovative approach led to significant improvements in F1 scores across various datasets. Okuboyejo and Olugbara [98] proposed a novel ensemble algorithm that leveraged residual networks and dual path networks, achieving high sensitivity, specificity, and balanced accuracy in skin lesion classification without needing prior segmentation for extremely imbalanced training datasets. Vidhyalakshmi and Kanchana [99] developed a hybrid flash butterfly optimized CNN, targeting early and accurate prediction of skin diseases from dermoscopic images.…”