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Intelligent skin cancer detection applying autoencoder, MobileNetV2 and spiking neural networks

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  • Toğaçar, Mesut
  • Cömert, Zafer
  • Ergen, Burhan

Abstract

Melanocytes are skin cells that give color to the skin and form melanin color pigments. The unbalanced division and proliferation of these cells result in skin cancer. The early diagnosis and proper treatment of skin cancer are so important. In this scope, a novel model that relies upon the autoencoder, spiking, and convolutional neural networks is proposed to ensure a useful decision support tool in this study. The experiments were carried out on an open-access dataset called the ISIC skin cancer consisting of 1800 being and 1497 malignant tumor images. In the proposed approach, the dataset is reconstructed using the autoencoder model. The original dataset and structured dataset were trained and classified by the MobileNetV2 model that consists of residual blocks, and the spiking networks. The classification success rate of the study was 95.27%. As a result, it was seen that the autoencoder model and spiking networks contributed to enhancing the performance of the MobileNetV2 model. Thanks to the proposed model, a novel fully automated decision support tool with high sensitivity was ensured for skin cancer detection.

Suggested Citation

  • Toğaçar, Mesut & Cömert, Zafer & Ergen, Burhan, 2021. "Intelligent skin cancer detection applying autoencoder, MobileNetV2 and spiking neural networks," Chaos, Solitons & Fractals, Elsevier, vol. 144(C).
    • Handle: RePEc:eee:chsofr:v:144:y:2021:i:c:s0960077921000679
    DOI: 10.1016/j.chaos.2021.110714
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    References listed on IDEAS

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    1. Xiangying Miao & Hongli Miao & Yongjun Jia & Yingting Guo, 2018. "Using a stacked-autoencoder neural network model to estimate sea state bias for a radar altimeter," PLOS ONE, Public Library of Science, vol. 13(12), pages 1-10, December.
    2. Xiurui Xie & Hong Qu & Guisong Liu & Malu Zhang & Jürgen Kurths, 2016. "An Efficient Supervised Training Algorithm for Multilayer Spiking Neural Networks," PLOS ONE, Public Library of Science, vol. 11(4), pages 1-29, April.
    3. Toğaçar, Mesut & Özkurt, Kutsal Baran & Ergen, Burhan & Cömert, Zafer, 2020. "BreastNet: A novel convolutional neural network model through histopathological images for the diagnosis of breast cancer," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
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    Cited by:

    1. Ahmad Naeem & Tayyaba Anees & Mudassir Khalil & Kiran Zahra & Rizwan Ali Naqvi & Seung-Won Lee, 2024. "SNC_Net: Skin Cancer Detection by Integrating Handcrafted and Deep Learning-Based Features Using Dermoscopy Images," Mathematics, MDPI, vol. 12(7), pages 1-35, March.

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