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Detecting skin cancer is a critical task for successful treatment and recovery. Unfortunately, traditional methods of diagnosis rely heavily on the expertise of dermatologists, whose availability can be limited. This paper proposed a novel approach using weight classification to balance the various skin cancer groupings and adjust for data anomalies. On the MNIST dataset to identify seven different types of skin lesions as skin cancer. A Convolutional Neural Network is designed to predict the type of cancerous and proposed model is further trained using a range of hyperparameter tuning to improve its accuracy. The proposed framework offers a promising result to early skin cancer detection with an accuracy of 91.75%.

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SCP-CNN: Skin Cancer Prediction on Imbalanced Dataset Using CNN Deep Learning

  • 会议论文

  • 发表日期：

    2025年

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  • Komal Gupta,
  • Monika Lamba,
  • Shraddha Arora

摘要

Detecting skin cancer is a critical task for successful treatment and recovery. Unfortunately, traditional methods of diagnosis rely heavily on the expertise of dermatologists, whose availability can be limited. This paper proposed a novel approach using weight classification to balance the various skin cancer groupings and adjust for data anomalies. On the MNIST dataset to identify seven different types of skin lesions as skin cancer. A Convolutional Neural Network is designed to predict the type of cancerous and proposed model is further trained using a range of hyperparameter tuning to improve its accuracy. The proposed framework offers a promising result to early skin cancer detection with an accuracy of 91.75%.