Convolutional Neural Network and Fuzzy Logic-based Hybrid Melanoma Diagnosis System

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dc.authoridYalcinkaya, Fikret/0000-0002-2174-918X
dc.contributor.authorYalcinkaya, Fikret
dc.contributor.authorErbas, Ali
dc.date.accessioned2025-01-21T16:37:12Z
dc.date.available2025-01-21T16:37:12Z
dc.date.issued2021
dc.departmentKırıkkale Üniversitesi
dc.description.abstractStudies on the detection of early stage melanoma have recently gained significant interest. Computer aided diagnosis systems based on neural networks, machine learning, convolutional neural networks (CNNs), and deep learning help early stage detection considerably. The colour and shapes of the images created by the pixels are crucial for the CNNs, as the pixels and associated pictures are interrelated just as a person's fingerprint is unique. By observing this relationship, the pixel values of each picture with its neighborhoods were determined by a fuzzy logic-based system and a unique fingerprint matrix named Fuzzy Correlation Map (FCov-Map) was produced. The fuzzy logic system has four inputs and one output. The advantage of CNNs trained with fuzzy covariance maps is to eliminate both the limited availability of medical grade training data and the need for extensive image preprocessing. The fuzzy logic output is fed to the pretrained AlexNet CNN algorithm. To deliver a reliable result, a deep CNN needs a large amount of data to process. However, to obtain and use the required sufficient data for diseases is not cost- and time-effective. Therefore, the suggested fuzzy logic-based fuzzy correlation map is tackling this issue to solve the limitedness of training CNN data set.
dc.identifier.doi10.5755/j02.eie.28843
dc.identifier.endpage77
dc.identifier.issn1392-1215
dc.identifier.issue2
dc.identifier.scopus2-s2.0-85105881568
dc.identifier.scopusqualityQ3
dc.identifier.startpage69
dc.identifier.urihttps://doi.org/10.5755/j02.eie.28843
dc.identifier.urihttps://hdl.handle.net/20.500.12587/24421
dc.identifier.volume27
dc.identifier.wosWOS:000646389200008
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherKaunas Univ Technology
dc.relation.ispartofElektronika Ir Elektrotechnika
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241229
dc.subjectFuzzy logic; Image segmentation; Medical diagnostic imaging; Neural networks
dc.titleConvolutional Neural Network and Fuzzy Logic-based Hybrid Melanoma Diagnosis System
dc.typeArticle

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