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<p>Melanoma skin cancer is an important, growing, and among the most lethal skin cancer forms, but could easily be cured if detected at an earlier stage. The primary cause of skin cancer is the abnormal growth of skin cells, often resulting from prolonged exposure to sunlight. Unfortunately, identifying malignant skin development at an early stage is both costly and challenging. The grading of skin cancer is determined based on the location of the tumor and the type of cells involved. Accurate classification of lesions requires a particular range of precision and recall, presenting a significant challenge in dermatology. This paper proposes a new system, the Composite Convolutional Neural Network (CCNN) skin cancer detector (SCD), based on the use of more sophisticated deep learning systems known as composite convolutional neural networks. The framework is applied in three primary stages. During the first step, noise, normalization, and increment of dermoscopic images are performed to improve the quality of data and variability. The next stage will utilize transfer learning using the models ResNet50, InceptionV3, and VGG16 to identify the high-level discriminative features. In the classification module, skin lesion classification is done using a customized CNN where the feature dimensionality reduction is done using an attention-based autoencoder to enhance the generalization of the model. Lastly, the deep features obtained are tested with convolutional machine learning classifiers, such as K - nearest neighbor (KNN), random forest classifier (RFC), and support-vector machine (SVM), which allow to compare the performance of the derived deep features; the accuracy of 93.53%, the precision of 94.02%, and the recall of 93.61% were achieved, which leads to the conclusion of the strength of the proposed approach and its possible use in clinical dermatologic diagnosis.</p>

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CCNN-SCD: a deep composite architecture for skin cancer detection using feature engineering-aided convolutional neural networks for dermatological diagnosis

  • 期刊论文

  • 发表日期：

    2026年2月25日

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  • Madiha Hameed,
  • Aneela Zameer Jaffery,
  • Muhammad Yousaf Hamza,
  • Muhammad Asif Zahoor Raja

摘要

Melanoma skin cancer is an important, growing, and among the most lethal skin cancer forms, but could easily be cured if detected at an earlier stage. The primary cause of skin cancer is the abnormal growth of skin cells, often resulting from prolonged exposure to sunlight. Unfortunately, identifying malignant skin development at an early stage is both costly and challenging. The grading of skin cancer is determined based on the location of the tumor and the type of cells involved. Accurate classification of lesions requires a particular range of precision and recall, presenting a significant challenge in dermatology. This paper proposes a new system, the Composite Convolutional Neural Network (CCNN) skin cancer detector (SCD), based on the use of more sophisticated deep learning systems known as composite convolutional neural networks. The framework is applied in three primary stages. During the first step, noise, normalization, and increment of dermoscopic images are performed to improve the quality of data and variability. The next stage will utilize transfer learning using the models ResNet50, InceptionV3, and VGG16 to identify the high-level discriminative features. In the classification module, skin lesion classification is done using a customized CNN where the feature dimensionality reduction is done using an attention-based autoencoder to enhance the generalization of the model. Lastly, the deep features obtained are tested with convolutional machine learning classifiers, such as K - nearest neighbor (KNN), random forest classifier (RFC), and support-vector machine (SVM), which allow to compare the performance of the derived deep features; the accuracy of 93.53%, the precision of 94.02%, and the recall of 93.61% were achieved, which leads to the conclusion of the strength of the proposed approach and its possible use in clinical dermatologic diagnosis.