Page 270 - 2024-Vol20-Issue2
P. 270
Received: 9 October 2023 | Revised: 6 December 2023 | Accepted: 21 December 2023
DOI: 10.37917/ijeee.20.2.23 Vol. 20 | Issue 2 | December 2024
Open Access
Iraqi Journal for Electrical and Electronic Engineering
Original Article
A Hybrid Lung Cancer Model for Diagnosis and Stage
Classification from Computed Tomography Images
Abdalbasit Mohammed Qadir*1, Peshraw Ahmed Abdalla2, Dana Faiq Abd1
1Information Technology Department, University of Human Development, Sulaymaniyah, Iraq
2Computer Science Department, University of Halabja, Halabja, Iraq
Correspondance
*Abdalbasit Mohammed Qadir
Information Technology Department,
University of Human Development, Sulaymaniyah, Iraq
Email: abdalbasit.mohammed@uhd.edu.iq
Abstract
Detecting pulmonary cancers at early stages is difficult but crucial for patient survival. Therefore, it is essential to
develop an intelligent, autonomous, and accurate lung cancer detection system that shows great reliability compared to
previous systems and research. In this study, we have developed an innovative lung cancer detection system known as
the Hybrid Lung Cancer Stage Classifier and Diagnosis Model (Hybrid-LCSCDM). This system simplifies the complex
task of diagnosing lung cancer by categorizing patients into three classes: normal, benign, and malignant, by analyzing
computed tomography (CT) scans using a two-part approach: First, feature extraction is conducted using a pre-trained
model called VGG-16 for detecting key features in lung CT scans indicative of cancer. Second, these features are
then classified using a machine learning technique called XGBoost, which sorts the scans into three categories. A
dataset, IQ-OTH/NCCD - Lung Cancer, is used to train and evaluate the proposed model to show its effectiveness. The
dataset consists of the three aforementioned classes containing 1190 images. Our suggested strategy achieved an overall
accuracy of 98.54%, while the classification precision among the three classes was 98.63%. Considering the accuracy,
recall, and precision as well as the F1-score evaluation metrics, the results indicated that when using solely computed
tomography scans, the proposed (Hybrid-LCSCDM) model outperforms all previously published models.
Keywords
Lung Cancer, CT Scan, Deep Learning, Diagnosis, Detection Model.
I. INTRODUCTION risk factor linked to economic expansion and globalization
[4]. According to the Global Cancer Statistics 2020 [5], in
Today, with the advent of deep learning techniques, the entire a comparison between all cancer types, the greatest fatality
healthcare sector has transformed with electronic diagnosis rate belongs to lung cancer patients, with an estimated annual
and treatment to aid patients and medical professionals during death rate of 1.80 million. The risk of mortality is further in-
the diagnostic and therapeutic phases of illnesses [1]. One of creased by inconsistent monitoring and care. Through diverse
the most significant health issues facing the globe today is can- methods such as classification, segmentation, and detection
cer [2]. The development of several genetic abnormalities and approaches, several researchers have tackled these problems
epigenetic alterations contribute to lung cancer, which causes with lung cancer detection [6, 7]. Artificial intelligence has
normal cells to proliferate out of control [3]. Every year, more become more important in healthcare and computer vision ap-
people worldwide pass away due to the disease. Based on the plications in recent years because of its superior performance
available data, it is estimated that the number of worldwide in prediction, detection, and suitability for categorization is-
cases of cancer could reach a total of 28.40 million by the sues. It is necessary to overcome the constraints of the current
year 2040. Nevertheless, this will be made worse by the rising
This is an open-access article under the terms of the Creative Commons Attribution License,
which permits use, distribution, and reproduction in any medium, provided the original work is properly cited.
©2024 The Authors.
Published by Iraqi Journal for Electrical and Electronic Engineering | College of Engineering, University of Basrah.
https://doi.org/10.37917/ijeee.20.2.23 |https://www.ijeee.edu.iq 266