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

Lung 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 ear...

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Main Authors: Jassim M.M., Jaber M.M.
Other Authors: 57843900500
Format: Article
Published: Technology Center 2023
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spelling my.uniten.dspace-271222023-05-29T17:39:51Z HYBRID SELECTION FRAMEWORK FOR CLASS BALANCING APPROACHES BASED ON INTEGRATED CNN AND DECISION MAKING TECHNIQUES FOR LUNG CANCER DIAGNOSIS Jassim M.M. Jaber M.M. 57843900500 56519461300 Lung 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 license Final 2023-05-29T09:39:51Z 2023-05-29T09:39:51Z 2022 Article 10.15587/1729-4061.2022.263644 2-s2.0-85137718412 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137718412&doi=10.15587%2f1729-4061.2022.263644&partnerID=40&md5=1c2b1f0bbc5fc0ae6a293c6632e16074 https://irepository.uniten.edu.my/handle/123456789/27122 4 9-118 69 76 All Open Access, Gold, Green Technology Center Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
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url_provider http://dspace.uniten.edu.my/
description Lung 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 license
author2 57843900500
author_facet 57843900500
Jassim M.M.
Jaber M.M.
format Article
author Jassim M.M.
Jaber M.M.
spellingShingle Jassim M.M.
Jaber M.M.
HYBRID SELECTION FRAMEWORK FOR CLASS BALANCING APPROACHES BASED ON INTEGRATED CNN AND DECISION MAKING TECHNIQUES FOR LUNG CANCER DIAGNOSIS
author_sort Jassim M.M.
title HYBRID SELECTION FRAMEWORK FOR CLASS BALANCING APPROACHES BASED ON INTEGRATED CNN AND DECISION MAKING TECHNIQUES FOR LUNG CANCER DIAGNOSIS
title_short HYBRID SELECTION FRAMEWORK FOR CLASS BALANCING APPROACHES BASED ON INTEGRATED CNN AND DECISION MAKING TECHNIQUES FOR LUNG CANCER DIAGNOSIS
title_full HYBRID SELECTION FRAMEWORK FOR CLASS BALANCING APPROACHES BASED ON INTEGRATED CNN AND DECISION MAKING TECHNIQUES FOR LUNG CANCER DIAGNOSIS
title_fullStr HYBRID SELECTION FRAMEWORK FOR CLASS BALANCING APPROACHES BASED ON INTEGRATED CNN AND DECISION MAKING TECHNIQUES FOR LUNG CANCER DIAGNOSIS
title_full_unstemmed HYBRID SELECTION FRAMEWORK FOR CLASS BALANCING APPROACHES BASED ON INTEGRATED CNN AND DECISION MAKING TECHNIQUES FOR LUNG CANCER DIAGNOSIS
title_sort hybrid selection framework for class balancing approaches based on integrated cnn and decision making techniques for lung cancer diagnosis
publisher Technology Center
publishDate 2023
_version_ 1806427615482871808
score 13.223943

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