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Skin cancer remains one of the most commonly diagnosed malignancies worldwide, where early and accurate detection is critical to improving patient outcomes. Dermoscopic image analysis has emerged as a critical tool for skin lesion classification. Traditional diagnostic methods rely heavily on visual assessment and histopathological analysis, which are time-consuming, subjective, and prone to inter-observer variability. Although recent computational approaches using convolutional neural networks (CNNs), hybrid models, and vision transformers have shown promise, they still struggle with key challenges such as class imbalance, inconsistent segmentation, and reduced generalizability across lesion types, limiting their clinical applicability. To overcome these limitations, we present an end-to-end framework that optimally combines preprocessing, segmentation, and classification to enhance performance. Our approach leverages a DermoGrabcut segmentation technique for more accurate segmentation. We employ a feature extraction approach that integrates Local Binary Patterns (LBP) and color histograms to capture essential textural and color features, thereby enhancing the performance of the subsequent classifier for more accurate predictions. We utilize classification test on Convolutional Neural Network (CNN) for robust classification, alongside comparative evaluations with Support Vector Machine (SVM), k-Nearest Neighbors (KNN), and Decision Trees (DT). We evaluate our model on the ISIC-2019 dataset, a comprehensive collection of dermoscopic images encompassing diverse lesion types and imbalanced class distribution. Results indicate that our proposed method using CNN model outperforms traditional classifiers and significantly outperforming existing methods, achieving high accuracy of 97.93%, a precision of 97.88%, and an F1-score of 96.80%. This approach successfully addresses the challenges of feature extraction and misclassification risk, providing an efficient and accurate solution for skin lesion classification for both clinical and telemedicine applications.

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Leveraging DermoGrabcut Segmentation for Improved CNN-Based Skin Lesion Classification

  • 会议论文

  • 发表日期：

    2025年

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  • Md Tanvir Islam,
  • Yunfei Yin,
  • Dayong Deng,
  • Md Minhazul Islam,
  • Syed Murtoza Mushrul Pasha

摘要

Skin cancer remains one of the most commonly diagnosed malignancies worldwide, where early and accurate detection is critical to improving patient outcomes. Dermoscopic image analysis has emerged as a critical tool for skin lesion classification. Traditional diagnostic methods rely heavily on visual assessment and histopathological analysis, which are time-consuming, subjective, and prone to inter-observer variability. Although recent computational approaches using convolutional neural networks (CNNs), hybrid models, and vision transformers have shown promise, they still struggle with key challenges such as class imbalance, inconsistent segmentation, and reduced generalizability across lesion types, limiting their clinical applicability. To overcome these limitations, we present an end-to-end framework that optimally combines preprocessing, segmentation, and classification to enhance performance. Our approach leverages a DermoGrabcut segmentation technique for more accurate segmentation. We employ a feature extraction approach that integrates Local Binary Patterns (LBP) and color histograms to capture essential textural and color features, thereby enhancing the performance of the subsequent classifier for more accurate predictions. We utilize classification test on Convolutional Neural Network (CNN) for robust classification, alongside comparative evaluations with Support Vector Machine (SVM), k-Nearest Neighbors (KNN), and Decision Trees (DT). We evaluate our model on the ISIC-2019 dataset, a comprehensive collection of dermoscopic images encompassing diverse lesion types and imbalanced class distribution. Results indicate that our proposed method using CNN model outperforms traditional classifiers and significantly outperforming existing methods, achieving high accuracy of 97.93%, a precision of 97.88%, and an F1-score of 96.80%. This approach successfully addresses the challenges of feature extraction and misclassification risk, providing an efficient and accurate solution for skin lesion classification for both clinical and telemedicine applications.