MELANOMA OF THE SKIN CANCER DIAGNOSIS USING SUPPORT VECTOR MACHINE | ||
| International Journal of Intelligent Computing and Information Sciences | ||
| Volume 24, Issue 1, March 2024, Pages 12-19 PDF (532.25 K) | ||
| Document Type: Original Article | ||
| DOI: 10.21608/ijicis.2024.260141.1313 | ||
| Authors | ||
| Zeina Rayan* 1; Islam Hegazy2; mohamed roushdy1; Abdel-Badeeh M. Salem1 | ||
| 1Computer Science Department, Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt | ||
| 2Department, Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt | ||
| Abstract | ||
| Cancer is one of the diseases, caused by cell divisions, that can be fatal. It has been the second most common cause of death for the past period of years globally. Any region of the body can be affected by a wide range of disorders collectively referred to as cancer. Neoplasms as well as malignant tumors are other terms that are used for describing cancer. Skin is one of the body parts that can be affected. Early melanoma skin cancer detection is a must so that the mortality rate of skin cancer patients is decreased. The accuracy of early detection of melanoma skin cancer can be enhanced through applying machine learning methods. This paper provides a model that can detect melanoma skin cancer early. This model is used using the dataset that the International Skin Imaging Collaboration has provided. The proposed model using support vector machines achieved a promising accuracy of 95.96%. | ||
| Keywords | ||
| Artificial Intelligence; Smart Health; Medical Informatics; Support Vector Machine; Convolution Neural Network | ||
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