Open Access

ARTICLE

An Intelligent Decision Support System for Lung Cancer Diagnosis

by Ahmed A. Alsheikhy^1,*, Yahia F. Said¹, Tawfeeq Shawly²

1 Electrical Engineering Department, College of Engineering, Northern Border University, Arar, Saudi Arabia
2 Electrical Engineering Department, Faculty of Engineering at Rabigh, King Abdulaziz University, Jeddah, Saudi Arabia

* Corresponding Author: Ahmed A. Alsheikhy. Email:

Computer Systems Science and Engineering 2023, 46(1), 799-817. https://doi.org/10.32604/csse.2023.035269

Received 15 August 2022; Accepted 28 October 2022; Issue published 20 January 2023

Abstract

Lung cancer is the leading cause of cancer-related death around the globe. The treatment and survival rates among lung cancer patients are significantly impacted by early diagnosis. Most diagnostic techniques can identify and classify only one type of lung cancer. It is crucial to close this gap with a system that detects all lung cancer types. This paper proposes an intelligent decision support system for this purpose. This system aims to support the quick and early detection and classification of all lung cancer types and subtypes to improve treatment and save lives. Its algorithm uses a Convolutional Neural Network (CNN) tool to perform deep learning and a Random Forest Algorithm (RFA) to help classify the type of cancer present using several extracted features, including histograms and energy. Numerous simulation experiments were conducted on MATLAB, evidencing that this system achieves 98.7% accuracy and over 98% precision and recall. A comparative assessment assessing accuracy, recall, precision, specificity, and F-score between the proposed algorithm and works from the literature shows that the proposed system in this study outperforms existing methods in all considered metrics. This study found that using CNNs and RFAs is highly effective in detecting lung cancer, given the high accuracy, precision, and recall results. These results lead us to believe that bringing this kind of technology to doctors diagnosing lung cancer is critical.

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Keywords

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