000110717 001__ 110717
000110717 005__ 20220908120613.0
000110717 0247_ $$2doi$$a10.18517/ijaseit.11.3.13679
000110717 0248_ $$2sideral$$a127349
000110717 037__ $$aART-2021-127349
000110717 041__ $$aeng
000110717 100__ $$aYanchatuña, O. P.
000110717 245__ $$aSkin lesion detection and classification using convolutional neural network for deep feature extraction and support vector machine
000110717 260__ $$c2021
000110717 5060_ $$aAccess copy available to the general public$$fUnrestricted
000110717 5203_ $$aPigmented skin lesion identification is essential for detecting harmful pathologies related to this large organ, especially cancer. An analysis of the different methods and projects developed to diagnose these illnesses throughout the years showed that they had become very useful tools to identify melanoma, dermatofibroma, and basal cell carcinoma, among other types of cancer, are seen through the use of new computer-aided technologies. The most common diagnosis is based on dermoscopy and the dermatologist expertise that can improve accuracy with image detection techniques and classification by computer. Therefore, this study aims to develop software models able to detect and classify skin cancer. The following work is based on the use of dermoscopy images obtained from the HAM10000 dataset, a database with 10000 images previously tested and validated for research use. The main process is divided into three relevant parts: image segmentation, feature extraction (FE) using ten different pre-trained Convolutional Neural Networks (CNNs), and Support Vector Machine (SVM) to establish a classification model. According to the results, the models of classification performed very well using the image segmentation step, showing average accuracies between 80.67% (Xception) and 90% (Alexnet). In contrast to the process without using image segmentation, where no method reached 60%. AlexNet plus SVM model showed the minor running time and presented the higher accuracy rate (90.34%) for the correct identification and classification of the seven categories of cutaneous lesions taken into account.
000110717 540__ $$9info:eu-repo/semantics/openAccess$$aby-sa$$uhttp://creativecommons.org/licenses/by-sa/3.0/es/
000110717 592__ $$a0.251$$b2021
000110717 594__ $$a1.8$$b2021
000110717 593__ $$aComputer Science (miscellaneous)$$c2021$$dQ3
000110717 593__ $$aAgricultural and Biological Sciences (miscellaneous)$$c2021$$dQ3
000110717 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000110717 700__ $$aPereira, J. P.
000110717 700__ $$aPila, K. O.
000110717 700__ $$aVásquez, P. A.
000110717 700__ $$aVeintimilla, K. S.
000110717 700__ $$aVillalba-Meneses, G. F.
000110717 700__ $$aAlvarado-Cando, O.
000110717 700__ $$aAlmeida-Galárraga, D.
000110717 773__ $$g11, 3 (2021), 1260-1267$$tInternational Journal on Advanced Science, Engineering and Information Technology$$x2088-5334
000110717 8564_ $$s1373935$$uhttps://zaguan.unizar.es/record/110717/files/texto_completo.pdf$$yVersión publicada
000110717 8564_ $$s2964061$$uhttps://zaguan.unizar.es/record/110717/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000110717 909CO $$ooai:zaguan.unizar.es:110717$$particulos$$pdriver
000110717 951__ $$a2022-09-08-11:58:55
000110717 980__ $$aARTICLE