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InSiNet: a deep convolutional approach to skin cancer detection and segmentation

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Abstract

Cancer is among the common causes of death around the world. Skin cancer is one of the most lethal types of cancer. Early diagnosis and treatment are vital in skin cancer. In addition to traditional methods, method such as deep learning is frequently used to diagnose and classify the disease. Expert experience plays a major role in diagnosing skin cancer. Therefore, for more reliable results in the diagnosis of skin lesions, deep learning algorithms can help in the correct diagnosis. In this study, we propose InSiNet, a deep learning-based convolutional neural network to detect benign and malignant lesions. The performance of the method is tested on International Skin Imaging Collaboration HAM10000 images (ISIC 2018), ISIC 2019, and ISIC 2020, under the same conditions. The computation time and accuracy comparison analysis was performed between the proposed algorithm and other machine learning techniques (GoogleNet, DenseNet-201, ResNet152V2, EfficientNetB0, RBF-support vector machine, logistic regression, and random forest). The results show that the developed InSiNet architecture outperforms the other methods achieving an accuracy of 94.59%, 91.89%, and 90.54% in ISIC 2018, 2019, and 2020 datasets, respectively. Since the deep learning algorithms eliminate the human factor during diagnosis, they can give reliable results in addition to traditional methods.

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Authors and Affiliations

  1. Department of Geomatics Engineering, Gumushane University, Gumushane, Turkey

    Hatice Catal Reis

  2. Gumushane University, Gumushane, Turkey

    Hatice Catal Reis

  3. Department of Computer Engineering, University of Harran, Sanliurfa, Turkey

    Veysel Turk

  4. Department of Infrastructure Engineering, University of Melbourne, Melbourne, Vic, 3052, Australia

    Kourosh Khoshelham

  5. Department of Geomatics Engineering, Gumushane University, Gumushane, Turkey

    Serhat Kaya

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  1. Hatice Catal Reis
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  2. Veysel Turk
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  4. Serhat Kaya
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Appendix 1 Details of the compared classifiers

Appendix 1 Details of the compared classifiers

Table 10 Detailed structure of the proposed InSiNet + FCC
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Table 11 Hyperparameters of machine learning algorithms
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Table 12 Hyperparameters of deep learning algorithms
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Table 13 Loss, accuracy, validation loss and validation accuracy values of the models
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Table 14 Loss, accuracy, validation loss and validation accuracy values of the MLP
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Fig. 17
figure 17

Architecture of the proposed InSiNet+FCC

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Fig. 18
figure 18

ROC curve for ISIC 2018 dataset

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Reis, H.C., Turk, V., Khoshelham, K. et al. InSiNet: a deep convolutional approach to skin cancer detection and segmentation. Med Biol Eng Comput 60, 643–662 (2022). https://doi.org/10.1007/s11517-021-02473-0

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