Open Access

ARTICLE

Melanoma Identification Through X-ray Modality Using Inception-v3 Based Convolutional Neural Network

Saad Awadh Alanazi^*

College of Computer and Information Sciences, Jouf University, Sakaka, Aljouf, 72341, Saudi Arabia

* Corresponding Author: Saad Awadh Alanazi. Email:

Computers, Materials & Continua 2022, 72(1), 37-55. https://doi.org/10.32604/cmc.2022.020118

Received 10 May 2021; Accepted 18 June 2021; Issue published 24 February 2022

Abstract

Melanoma, also called malignant melanoma, is a form of skin cancer triggered by an abnormal proliferation of the pigment-producing cells, which give the skin its color. Melanoma is one of the skin diseases, which is exceptionally and globally dangerous, Skin lesions are considered to be a serious disease. Dermoscopy-based early recognition and detection procedure is fundamental for melanoma treatment. Early detection of melanoma using dermoscopy images improves survival rates significantly. At the same time, well-experienced dermatologists dominate the precision of diagnosis. However, precise melanoma recognition is incredibly hard due to several factors: low contrast between lesions and surrounding skin, visual similarity between melanoma and non-melanoma lesions, and so on. Thus, reliable automatic detection of skin tumors is critical for pathologists’ effectiveness and precision. To take care of this issue, numerous research centers around the world are creating autonomous image processing-oriented frameworks. We suggested deep learning methods in this article to address significant tasks that have emerged in the field of skin lesion image processing: we provided a Convolutional Neural Network (CNN) based framework using an Inception-v3 (INCP-v3) melanoma detection scheme and accomplished very high precision (98.96%) against melanoma detection. The classification framework of CNN is created utilizing TensorFlow and Keras in the backend (in Python). It likewise utilizes Transfer-Learning (TL) approach. It is prepared on the data gathered from the “International Skin Imaging Collaboration (ISIC)” repositories. The experiments show that the suggested technique outperforms state-of-the-art methods in terms of predictive performance.

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Keywords

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