International Journal of Electrical and Computer Engineering (IJECE) Vol. 13, No. 5, October 2023, pp. 5792~5803 ISSN: 2088-8708, DOI: 10.11591/ijece.v13i5.pp5792-5803  5792 Journal homepage: http://ijece.iaescore.com A deep convolutional structure-based approach for accurate recognition of skin lesions in dermoscopy images Shimaa Fawzy 1 , Hossam El-Din Moustafa 1 , Ehab H. AbdelHay 1 , Mohamed Maher Ata 2 1 Department of Communications and Electronics Engineering, Faculty of Engineering, Mansoura University, Mansoura, Egypt 2 Department of Communications and Electronics Engineering, MISR Higher Institute for Engineering and Technology, Mansoura, Egypt Article Info ABSTRACT Article history: Received Feb 26, 2023 Revised Apr 11, 2023 Accepted Apr 14, 2023 One-third of all cancer diagnoses worldwide are skin malignancies. One of the most common tumors, skin cancer can develop from a variety of dermatological conditions and is subdivided into different categories based on its textile, color, body, and other morphological characteristics. The most effective strategy to lower the mortality rate of melanoma is early identification because skin cancer incidence has been on the rise recently. In order to categorize dermoscopy images into the four diagnosis classifications of melanoma, benign, malignant, and human against machine (HAM) not melanoma, this research suggests a computer-aided diagnosis (CAD) system. Experimental results show that the suggested approach enabled 97.25% classification accuracy. In order to automate the identification of skin cancer and expedite the diagnosis process in order to save a life, the proposed technique offers a less complex and cutting-edge framework. Keywords: Classification Convolutional neural network Deep learning Feature extraction Preprocessing Skin cancer This is an open access article under the CC BY-SA license. Corresponding Author: Mohamed Maher Ata Department of Communications and Electronics Engineering, MISR Higher Institute for Engineering and Technology Mansoura-35516, Egypt Email: mmaher844@yahoo.com 1. INTRODUCTION One of the major cancers, skin cancer, has had a rising prevalence over the past skin cancer is one of the worst cancers and is the most common variety in the world. Over the past few decades, its prevalence has increased. The aberrant expansion of cells is linked to the development of skin cancer. Melanoma, malignant, human against machine (HAM), and the International Skin Imaging Collaboration (ISIC) are a few examples of the various kinds of skin cancer. The most aggressive form of cancer among these several types is melanoma, which spreads swiftly throughout the body, has a tendency to spread early, and often takes many lives if it is discovered in the later stages. The presence of moles is a risk factor for melanoma. Most people have benign moles or nevi, but some can increase the risk of melanoma. An expert dermatologist must compare different skin lesions in order to make the diagnosis of skin cancer. Effective illness management and therapy are made easier by prompt diagnosis [1]. Although cancer can exist anywhere on the body, skin cancer is a frequent kind that often manifests in the skin that has been exposed to sunlight on a regular basis. Skin cancer is quite obvious since it starts in the epidermis, the top layer of skin [2]. This shows that computer-aided diagnosis (CAD) systems may use photos of skin lesions to make a preliminary diagnosis without considering any other pertinent data. The performance of the dermoscopy imaging approach improved by 50%, aiding the specialist in the early diagnosis of some kinds of skin cancer.