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Designing a new deep convolutional neural network for skin lesion recognition

  • Track 2: Medical Applications of Multimedia
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Abstract

In recent decades, many people have died because of cancer. Like many diseases, early detection of the disease can greatly help the healing process of the sick people. One of the most common types of cancer is the skin cancer. Skin lesions can lead to a kind of malignant cancer. Hence, it is important to diagnose the type of the skin lesion. Mostly, the skin lesion diagnosis needs to be done by an expert dermatologist. But a dermatologist’s examination can be time consuming and inaccurate. Therefore, using modern computer aided diagnosis methods can help to increase the speed and accuracy of the diagnosis of this disease. This paper proposes a new approach for skin lesion recognition using a new Convolutional Neural Network (CNN) with 69 layers. The proposed method has been tested on three benchmark datasets of skin lesions including PH2, ISIC 2016, and ISIC 2017. The experimental results illustrate that the proposed method performs better compared to the state-of-the-art in the field. Our method reached to 97.2%, 96.3%, and 99.4% of accuracy on PH2, ISIC2016, and ISIC2017 datasets, respectively.

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It is necessary to mention that, Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

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Authors and Affiliations

  1. Department of Computer Engineering, Malayer University, P. O. Box 65719-95863, Malayer, Iran

    Homayoun Rastegar & Davar Giveki

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  1. Homayoun Rastegar
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Correspondence to Davar Giveki.

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Rastegar, H., Giveki, D. Designing a new deep convolutional neural network for skin lesion recognition. Multimed Tools Appl 82, 18907–18923 (2023). https://doi.org/10.1007/s11042-022-14181-4

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