Skin Cancer Detection through Image Analysis with a Dual- Architecture Deep Learning Approach

Abstract

This research paper addresses global skin cancer concerns, emphasising the need for early detection through artificial intelligence (AI), specifically leveraging AlexNet and EfficientNet. The study highlights limitations in traditional diagnostic methods and proposes an AI-driven paradigm shift. In this paper details of the development of a deep learning-based approach have been presented, wherein existing differentiating images are augmented, covering data pre-processing, training, and evaluation with rigorous scientific methodology that utilises the parametric classification of a skin cancer-based lesion or visible marking. Findings emphasise the efficacy of both models, envisioning a future where AI and image-based observation play pivotal roles in early skin cancer detection. The AlexNet architecture receives a 98.9% accuracy, while the EfficientNet B1 provides an accuracy of 88.9%. The conclusion underscores the transformative potential of AI and a productive way of combining architecture to multiply the efficacy in skin cancer detection, leading the way for increased accuracy and accessibility in early diagnosis.

DOI: https://doi.org/10.24321/3051.4266.202601