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<p>Skin conditions affect millions globally, impacting physical health and quality of life. The accurate classification of skin lesions is critical in dermatology for timely diagnosis and effective treatment, significantly impacting patient outcomes. This paper presents a novel methodology, termed Modified Deep Joint Segmentation with DCNN-SQN Model, designed at improving the classification accuracy of skin lesions through a multi-step process. At first, the input images are pre-processing by Improved Conv.NADE method to enhance their quality. Subsequently, a Modified Deep Joint segmentation model is developed to accurately delineate lesion boundaries. Features essential for classification, including Multi Texton features, shape attributes, and Statistical features, are then retrieved from the segmented regions. Data augmentation methods are utilized to enlarge the data, followed by classification utilizing a hybrid model that integrates DCNN and SqueezeNet architectures. Additionally, an Improved score level fusion technique is implemented to optimize the integration of outputs from both networks, thereby enhancing overall classification performance. The proposed methodology is implemented and evaluated using Python, with comparisons made against conventional methods across diverse criteria, including accuracy, sensitivity, precision, MCC, FNR, and NPV. The results indicate promising advancements in skin lesion classification, providing a robust framework for early detection and intervention.</p>

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Modified Deep Joint Segmentation with DCNN-SQN Model for Skin Lesion Classification

  • 期刊论文

  • 发表日期：

    2026年6月16日

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  • Sadanand S. Howal,
  • S. J. Wagh

摘要

Skin conditions affect millions globally, impacting physical health and quality of life. The accurate classification of skin lesions is critical in dermatology for timely diagnosis and effective treatment, significantly impacting patient outcomes. This paper presents a novel methodology, termed Modified Deep Joint Segmentation with DCNN-SQN Model, designed at improving the classification accuracy of skin lesions through a multi-step process. At first, the input images are pre-processing by Improved Conv.NADE method to enhance their quality. Subsequently, a Modified Deep Joint segmentation model is developed to accurately delineate lesion boundaries. Features essential for classification, including Multi Texton features, shape attributes, and Statistical features, are then retrieved from the segmented regions. Data augmentation methods are utilized to enlarge the data, followed by classification utilizing a hybrid model that integrates DCNN and SqueezeNet architectures. Additionally, an Improved score level fusion technique is implemented to optimize the integration of outputs from both networks, thereby enhancing overall classification performance. The proposed methodology is implemented and evaluated using Python, with comparisons made against conventional methods across diverse criteria, including accuracy, sensitivity, precision, MCC, FNR, and NPV. The results indicate promising advancements in skin lesion classification, providing a robust framework for early detection and intervention.