Enhanced Skin Cancer Classification with MobileNetV3 and Morphological Preprocessing: A Deep Learning-Based Extension

Sidra Mehboob; Maryam Bukhari; Yaser Ali Shah; Salabat Khan; Muhammad Sharif

Authors

Sidra Mehboob Department of Computer Science COMSATS University Islamabad, Attock Campus Attock 43600, Pakistan
Maryam Bukhari Department of Computer Science COMSATS University Islamabad, Attock Campus Attock 43600, Pakistan
Yaser Ali Shah Department of Computer Science COMSATS University Islamabad, Attock Campus Attock 43600, Pakistan
Salabat Khan Big Data Research Center Jeju National University Jeju 63243, Republic of Korea
Muhammad Sharif Department of Computer Science COMSATS University Islamabad, Attock Campus Attock 43600, Pakistan

Keywords:

Skin Cancer Detection, Transfer Learning, MobileNetV3, Mathematical Morphology, Dermoscopic Image Classification

Abstract

Skin cancer detection continues to pose challenges due to the visual similarity between the types of lesions and the limitations of traditional diagnostic methods. This study presents an extended and improved skin lesion classification framework that combines transfer learning with MobileNetV3 and enhanced preprocessing using mathematical morphological techniques. These preprocessing methods refine lesion boundaries and suppress irrelevant structures in dermoscopic images, thereby improving feature discrimination during training. The refined framework is evaluated using the ISIC dataset and achieves a notable classification accuracy of 89%, showing superior performance compared to baseline models. This extension also examines the generalizability and suitability of the model for deployment in low-resource mobile settings. The results validate the effectiveness of lightweight architectures paired with morphological enhancements, providing a reliable and scalable solution for early skin cancer screening and clinical support.

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