Data Modeling
Mathematical and Computational Data Modeling
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Volume 5 Number 1 2024
RESEARCH ARTICLE (Open Access)
Enhancing Skin Cancer Diagnosis: A Comparative Analysis of Transfer Learning Techniques for Multi-Class Skin Cancer Classification
Mohammad Nazmush Shamael 1*, Khondaker Abdullah Al Mamun 1
Data Modeling 5 (1) 1-8 https://doi.org/10.25163/data.5110853
Submitted: 25 September 2024 Revised: 25 September 2024 Accepted: 25 September 2024 Published: 25 September 2024
Abstract
Skin cancer is a prevalent and potentially life-threatening disease, emphasizing the significance of early detection for patient recovery and survival. Timely treatment in the early stages of skin cancer can result in a survival rate exceeding 99% over a five-year period. Melanoma, the most dangerous form of skin cancer, requires prompt diagnosis to prevent its spread and the need for more aggressive treatments. Computer-aided methods, particularly using deep learning, have shown promise in improving the accuracy and efficiency of skin lesion diagnosis. With the advent of AI-based applications and advancements in smartphone technology, low-cost and accessible screening options are becoming available. As pre-trained transfer learning models are becoming more prevalent in the image classification space, it can be a cost effective way to create and train skin cancer screening options. This work conducts a comparative analysis of five distinct transfer learning models for the multi-class classification of skin cancer using the HAM10000 dataset. We used the VGG19 (Simonyan & Zisserman, 2014), ResNet50 (He et al., 2016), MobileNet (Howard et al., 2017), Inception-ResNetV2 (Szegedy et al., 2017), and InceptionV3 (Szegedy et al., 2016) models for the transfer learning models. Results show that the MobileNet model achieves the highest accuracy, recall, precision, and F-measure scores. The work includes a review of existing machine learning approaches, a description of the proposed model's methodology, analysis of qualitative results, and concludes with future research directions.
Keywords: Machine Learning, Deep learning, Transfer learning, Skin lesion screening
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