چكيده به لاتين
Abstract
Melanoma is one of the most deadly malignant skin cancers that is diagnosed early and in the early stages, the chances of survival increase and failure to diagnose it leads to the death of the patient. In this study, by classifying skin images resulting from various types of moles and lesions observed on the skin, we seek to diagnose whether they are cancerous or not without the use of painful and costly sampling surgery.After performing some preprocessing such as reducing the size of images, converting color images to gray images, etc., the suspicious area is identified using the U-Net algorithm. Then, using deep learning, some effective features in classification such as edge, shape, color, etc. have been extracted. The extracted features are considered as input of support vector machine methods, K-nearest neighbor, logistic regression and one of the deep learning methods called InceptionResentV2. In this study, the samples are classified into two classes of melanoma and non-melanoma. The results show that the deep learning method has a higher speed and accuracy compared to machine learning methods and has an accuracy of 92.58. In logistic regression method, three different types of extracted features are used as input; A. Utilization of all features of the deep learning network, b. Using the features obtained by using the default functions of Python software, c. Using the characteristics obtained from applying the least scattering criterion, the accuracy of 87.59, 87.63 and 86.47 were obtained for these three modes, respectively. The best performance of the K-nearest neighbor algorithm was also obtained for 50 with a resolution of 75.48.Besides, the backup vector machine algorithm was not able to train and provide a suitable model for classifying the samples and could not achieve convergence during training.
Keywords: Skin cancer, Melanoma, Machine Learning, Feature Extraction, Deep Learning
