Hybrid selection framework for class balancing approaches based on integrated CNN and decision making techniques for lung cancer diagnosis

Authors

DOI:

https://doi.org/10.15587/1729-4061.2022.263644

Keywords:

lung cancer, deep learning, multi-criteria decision making (MCDM), class imbalance

Abstract

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.

Author Biographies

Mustafa Mohammed Jassim, Iraqi Commission for Computers and Informatics (ICCI); Al-Farahidi University

MSc, Researcher

Informatics Institute for Postgraduate Studies (IIPS)

Department of Medical Instruments Engineering Techniques

Mustafa Musa Jaber, Dijlah University College; Universiti Tenaga Nasional

PhD, Lecturer

Department of Medical Instruments Engineering Techniques

Institute of Informatics and Computing in Energy

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Published

2022-08-31

How to Cite

Jassim, M. M., & Jaber, M. M. (2022). Hybrid selection framework for class balancing approaches based on integrated CNN and decision making techniques for lung cancer diagnosis. Eastern-European Journal of Enterprise Technologies, 4(9(118), 69–76. https://doi.org/10.15587/1729-4061.2022.263644

Issue

Vol. 4 No. 9(118) (2022): Information and controlling system

Section

Information and controlling system

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Copyright (c) 2022 Mustafa Mohammed Jassim, Mustafa Musa Jaber

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