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HYBRID SELECTION FRAMEWORK FOR CLASS BALANCING APPROACHES BASED ON INTEGRATED CNN AND DECISION MAKING TECHNIQUES FOR LUNG CANCER DIAGNOSIS

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dc.citedby1
dc.contributor.authorJassim M.M.en_US
dc.contributor.authorJaber M.M.en_US
dc.contributor.authorid57843900500en_US
dc.contributor.authorid56519461300en_US
dc.date.accessioned2023-05-29T09:39:51Z
dc.date.available2023-05-29T09:39:51Z
dc.date.issued2022
dc.description.abstractLung cancer is the fastest-growing and most dangerous type of cancer worldwide. It ranks first among cancer diseases in the number of deaths, and diagnosing it at late stages makes treatment more difficult. Artificial intelligence has played an essential role in the medical field in general, and early diagnosis of diseases and analyzing medical images in particular, as it can reduce human errors that may occur with the medical expert in medical image analysis. In this study, a hybrid framework is proposed between deep learning using the proposed convolutional neural network and multi-criteria decision-making techniques in order to reach an effective and accurate classification model for lung cancer diagnosis and select the best methodology to solve the problem of class imbalance datasets, which is a general problem in medical data that causes problems and errors in prediction. The IQ-OTHNCCD dataset that has a class imbalance was used. Three class balancing techniques were used separately and the data from each one enters the proposed convolutional neural network for feature extraction and classification. Then the Fuzzy-Weighted Zero-Inconsistency algorithm and VIKOR were used to make the ranking for the best classification approach and determine the best technique to balance the classes. This contributed to increasing the efficiency of the classification, where the best model got an accuracy of 99.27 %, sensitivity of 99.33 %, specificity of 99 %, precision of 98.67 % and F1-score of 99 %. This study can be applied to any data that suffers from the class imbalance problem to find the best technique that gives the highest classification accuracy � 2022, Authors. This is an open access article under the Creative Commons CC BY licenseen_US
dc.description.natureFinalen_US
dc.identifier.doi10.15587/1729-4061.2022.263644
dc.identifier.epage76
dc.identifier.issue9-118
dc.identifier.scopus2-s2.0-85137718412
dc.identifier.spage69
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85137718412&doi=10.15587%2f1729-4061.2022.263644&partnerID=40&md5=1c2b1f0bbc5fc0ae6a293c6632e16074
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/27122
dc.identifier.volume4
dc.publisherTechnology Centeren_US
dc.relation.ispartofAll Open Access, Gold, Green
dc.sourceScopus
dc.sourcetitleEastern-European Journal of Enterprise Technologies
dc.titleHYBRID SELECTION FRAMEWORK FOR CLASS BALANCING APPROACHES BASED ON INTEGRATED CNN AND DECISION MAKING TECHNIQUES FOR LUNG CANCER DIAGNOSISen_US
dc.typeArticleen_US
dspace.entity.typePublication
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