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<p>Nowadays, skin cancer is one of the most common cancers, and early diagnosis has a significant influence on improving the outcome of the patient. In this paper, a novel hybrid Deep Learning (DL) based approach is presented for the classification of skin lesions where both Convolutional Neural Networks (CNNs) as well as architectures-based on Transformer are implemented together. As such, the primary goal of the proposed model would be to enhance both the effectiveness and efficiency of the diagnosis of skin lesions by focusing on spatial as well as contextual features of the images. The CNN layer extracts rich features from the input images of skin lesions, while the Transformer layer captures the longer dependencies and contextual information within the images, which consequently allows for better distinguishing between benign and malignant lesions. Moreover, the model is further enhanced with clinical metadata pertinent to patient information to increase the accuracy of the diagnostic process. To examine the efficacy of the proposed approach, an extensive experiment was conducted using a publicly available skin lesion dataset (HAM10000-2018, ISIC-2019, and DermNet-2019); further comparisons were done with traditional CNN-based classifiers. The results attained by hybrid model overperform traditional classification performance in terms of diagnosis and efficiency. Advancement in the automation of medical image analysis provides us with benefits and strengths of using both CNNs and Transformers in the classification of skin lesions.</p>

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A hybrid deep learning approach for skin lesion classification using convolutional neural networks and transformers

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月19日

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  • Arpit Kumar Sharma,
  • Praveen Kumar Bhanodia,
  • Megha Bhushan

摘要

Nowadays, skin cancer is one of the most common cancers, and early diagnosis has a significant influence on improving the outcome of the patient. In this paper, a novel hybrid Deep Learning (DL) based approach is presented for the classification of skin lesions where both Convolutional Neural Networks (CNNs) as well as architectures-based on Transformer are implemented together. As such, the primary goal of the proposed model would be to enhance both the effectiveness and efficiency of the diagnosis of skin lesions by focusing on spatial as well as contextual features of the images. The CNN layer extracts rich features from the input images of skin lesions, while the Transformer layer captures the longer dependencies and contextual information within the images, which consequently allows for better distinguishing between benign and malignant lesions. Moreover, the model is further enhanced with clinical metadata pertinent to patient information to increase the accuracy of the diagnostic process. To examine the efficacy of the proposed approach, an extensive experiment was conducted using a publicly available skin lesion dataset (HAM10000-2018, ISIC-2019, and DermNet-2019); further comparisons were done with traditional CNN-based classifiers. The results attained by hybrid model overperform traditional classification performance in terms of diagnosis and efficiency. Advancement in the automation of medical image analysis provides us with benefits and strengths of using both CNNs and Transformers in the classification of skin lesions.