Enhancing Skin Cancer Detection: A Comparative Analysis of Models with VGG-16, VGG-19, and Inception V3
Keywords:
VGG-19, practitioners, false-positive rate, F-ScoreAbstract
In the realm of skin cancer-related fatalities, early detection of malignant lesions is the key to effective treatment and saving lives. While deep learning approach have shown promise in cancer detection, the effectiveness of individual models can be limiting. In this article, we explore the potential of ensemble models to enhance the performance of skin cancer detection. We present an ensemble model designed to identify skin cancer, leveraging the power of three well-established deep learning design: VGG-16, VGG-19, and Inception V3. By comparing the performance of these models, we shed light on their strengths and weaknesses in this critical domain. Our findings reveal that the suggested ensemble model, with a particular emphasis on VGG-16, exhibits an impressive average accuracy of 92%. Notably, when compared to VGG-19 and Inception V3, the suggested VGG-16 model outperforms in various crucial aspects. It excels in terms of sensitivity, accuracy, F-Score, specificity, false-positive rate, and precision, create it a promising choice for accurate and genuine skin cancer detection. In the pursuit of improving early cancer diagnosis, this research underscores the potential of ensemble models and highlights the pivotal role played by the VGG-16 architecture. These results provide valuable insights for the medical community and deep learning practitioners, with the ultimate goal of enhancing skin cancer detection methods and saving lives.
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