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

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dc.contributor.authorGhazal, Taher M.
dc.contributor.authorHussain, Sajid
dc.contributor.authorKhan, Muhammad Farhan
dc.contributor.authorKhan, Muhammad Adnan
dc.contributor.authorSaid, Raed A. T.
dc.contributor.authorAhmad, Munir
dc.date.accessioned2022-04-25T11:24:43Z
dc.date.available2022-04-25T11:24:43Z
dc.date.copyright© 2022
dc.date.issued2022
dc.description.abstractSkin 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.citationGhazal, 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/10.1155/2022/4826892en_US
dc.identifier.issn16875265
dc.identifier.urihttps://doi.org/10.1155/2022/4826892
dc.identifier.urihttp://hdl.handle.net/20.500.12519/552
dc.language.isoenen_US
dc.publisherHindawi Limiteden_US
dc.relationAuthors 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.ispartofseriesComputational Intelligence and Neuroscience;Volume 2022
dc.rightsCreative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
dc.rights.holderCopyright : © 2022 Taher M.
dc.rights.holderCopyright : © 2022 Taher M.
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectHumansen_US
dc.subjectMachine Learningen_US
dc.subjectNeoplasmsen_US
dc.subjectNeural Networksen_US
dc.subjectComputeren_US
dc.subjectSkinen_US
dc.subjectConvolutional neural networksen_US
dc.subjectDeep neural networksen_US
dc.subjectDiagnosisen_US
dc.subjectDiseasesen_US
dc.subjectMultilayer neural networksen_US
dc.subjectPatient treatmenten_US
dc.subjectTumorsen_US
dc.subjectBenign and malignant tumorsen_US
dc.subjectBenign tumouren_US
dc.subjectDiagnose diseaseen_US
dc.subjectDiagnose systemen_US
dc.subjectLikelihood ratiosen_US
dc.subjectMalignant tumorsen_US
dc.subjectSkin cancersen_US
dc.subjectSurvival ratioen_US
dc.subjectSystem modelsen_US
dc.subjectTransfer learningen_US
dc.subjectDermatologyen_US
dc.titleDetection of Benign and Malignant Tumors in Skin Empowered with Transfer Learningen_US
dc.typeArticleen_US

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