Detection of Benign and Malignant Tumors in Skin Empowered with Transfer Learning

dc.contributor.author Ghazal, Taher M.
dc.contributor.author Hussain, Sajid
dc.contributor.author Khan, Muhammad Farhan
dc.contributor.author Khan, Muhammad Adnan
dc.contributor.author Said, Raed A. T.
dc.contributor.author Ahmad, Munir
dc.date.accessioned 2022-04-25T11:24:43Z
dc.date.available 2022-04-25T11:24:43Z
dc.date.copyright © 2022
dc.date.issued 2022
dc.description This article is not available at CUD collection. The version of scholarly record of this paper is published in Computational Intelligence and Neuroscience (2022), available online at: https://doi.org/10.1155/2022/4826892 en_US
dc.description.abstract Skin cancer is a major type of cancer with rapidly increasing victims all over the world. It is very much important to detect skin cancer in the early stages. Computer-developed diagnosis systems helped the physicians to diagnose disease, which allows appropriate treatment and increases the survival ratio of patients. In the proposed system, the classification problem of skin disease is tackled. An automated and reliable system for the classification of malignant and benign tumors is developed. In this system, a customized pretrained Deep Convolutional Neural Network (DCNN) is implemented. The pretrained AlexNet model is customized by replacing the last layers according to the proposed system problem. The softmax layer is modified according to binary classification detection. The proposed system model is well trained on malignant and benign tumors skin cancer dataset of 1920 images, where each class contains 960 images. After good training, the proposed system model is validated on 480 images, where the size of images of each class is 240. The proposed system model is analyzed using the following parameters: accuracy, sensitivity, specificity, Positive Predicted Values (PPV), Negative Predicted Value (NPV), False Positive Ratio (FPR), False Negative Ratio (FNR), Likelihood Ratio Positive (LRP), and Likelihood Ratio Negative (LRN). The accuracy achieved through the proposed system model is 87.1%, which is higher than traditional methods of classification. © 2022 Taher M. en_US
dc.identifier.citation Ghazal, T. M., Hussain, S., Khan, M. F., Khan, M. A., Said, R. A. T., & Ahmad, M. (2022). Detection of benign and malignant tumors in skin empowered with transfer learning. Computational Intelligence and Neuroscience, 2022. https://doi.org/10.1155/2022/4826892 en_US
dc.identifier.issn 16875265
dc.identifier.uri https://doi.org/10.1155/2022/4826892
dc.identifier.uri http://hdl.handle.net/20.500.12519/552
dc.language.iso en en_US
dc.publisher Hindawi Limited en_US
dc.relation Authors Affiliations : Ghazal, T.M., Center for Cyber Security, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia (UKM), 43600 BangiSelangor, Malaysia, School of Information Technology, Skyline University College, University City Sharjah, Sharjah, 1797, United Arab Emirates; Hussain, S., Riphah School of Computing and Innovation, Faculty of Computing, Riphah International University, Lahore Campus, Lahore, 54000, Pakistan; Khan, M.F., Department of Forensic Sciences, University of Health Sciences, Lahore, 54000, Pakistan; Khan, M.A., Riphah School of Computing and Innovation, Faculty of Computing, Riphah International University, Lahore Campus, Lahore, 54000, Pakistan, Pattern Recognition and Machine Learning, Department of Software, Gachon University, South Korea; Said, R.A.T., Faculty of Management, Canadian UniversityDubai 117781, United Arab Emirates; Ahmad, M., School of Computer Science, National College of Business Administration and Economics, Lahore, 54000, Pakistan
dc.relation.ispartofseries Computational Intelligence and Neuroscience;Volume 2022
dc.rights Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
dc.rights.holder Copyright : © 2022 Taher M.
dc.rights.holder Copyright : © 2022 Taher M.
dc.rights.uri http://creativecommons.org/licenses/by/4.0/
dc.subject Humans en_US
dc.subject Machine Learning en_US
dc.subject Neoplasms en_US
dc.subject Neural Networks en_US
dc.subject Computer en_US
dc.subject Skin en_US
dc.subject Convolutional neural networks en_US
dc.subject Deep neural networks en_US
dc.subject Diagnosis en_US
dc.subject Diseases en_US
dc.subject Multilayer neural networks en_US
dc.subject Patient treatment en_US
dc.subject Tumors en_US
dc.subject Benign and malignant tumors en_US
dc.subject Benign tumour en_US
dc.subject Diagnose disease en_US
dc.subject Diagnose system en_US
dc.subject Likelihood ratios en_US
dc.subject Malignant tumors en_US
dc.subject Skin cancers en_US
dc.subject Survival ratio en_US
dc.subject System models en_US
dc.subject Transfer learning en_US
dc.subject Dermatology en_US
dc.title Detection of Benign and Malignant Tumors in Skin Empowered with Transfer Learning en_US
dc.type Article en_US
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