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Skin cancer is among the most important health challenges worldwide. They are leading to high death rates and affecting millions of people and it is a convenient fact that reliable and effective screening methods are urgently required for better treatment outcomes and early detection. Due to harmful UV rays that may cause both melanoma and non-melanoma skin cancer, the growth in cases may cause issues for the healthcare sector. The main challenge in detecting skin cancer is making early diagnoses easier and more affordable while maintaining or improving detection quality. Automated systems are required to analyze the dermoscopic images. This work proposes an automated system to determine whether a patient has skin cancer, specifically focusing on melanoma detection using the MobileNet deep learning model, which is a type of Convolutional Neural Network designed for efficiency.

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Automated Skin Lesion Detection using Deep Learning Approach

  • 会议论文

  • 发表日期：

    2026年

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  • Sreekara Karthik Gundlapalli,
  • Gnanesh Vaka,
  • P. Saranya

摘要

Skin cancer is among the most important health challenges worldwide. They are leading to high death rates and affecting millions of people and it is a convenient fact that reliable and effective screening methods are urgently required for better treatment outcomes and early detection. Due to harmful UV rays that may cause both melanoma and non-melanoma skin cancer, the growth in cases may cause issues for the healthcare sector. The main challenge in detecting skin cancer is making early diagnoses easier and more affordable while maintaining or improving detection quality. Automated systems are required to analyze the dermoscopic images. This work proposes an automated system to determine whether a patient has skin cancer, specifically focusing on melanoma detection using the MobileNet deep learning model, which is a type of Convolutional Neural Network designed for efficiency.