Issue |
E3S Web Conf.
Volume 391, 2023
4th International Conference on Design and Manufacturing Aspects for Sustainable Energy (ICMED-ICMPC 2023)
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Article Number | 01183 | |
Number of page(s) | 8 | |
DOI | https://doi.org/10.1051/e3sconf/202339101183 | |
Published online | 05 June 2023 |
Improving the Accuracy in Lung Cancer Detection Using NN Classifier
1 ECE Department, Lakireddy Bali Reddy College of Engineering (Autonomous), Mylavaram, Krishna District - 521230, Andhra Pradesh, India
2 Department of Electronics & Communication Engineering, Rajeev Gandhi Memorial College of Engineering and Technology, Nandyal, Andhra Pradesh, 518501 INDIA
3 Department of Electronics & Communication Engineering, Santhiram Engineering College, Nandyal, Andhra Pradesh, 518501 INDIA
4 Computer Technical Engineering Department, College of Technical Engineering, The Islamic University, Najaf, Iraq
5 Uttaranchal School of Computing Sciences, Uttaranchal University, Dehradun 248007 INDIA
* Corresponding author: lingamurthy@gmail.com
Lung cancer is a leading cause of cancer-related deaths worldwide, with a high mortality rate and a significant economic burden on health care systems. Traditional screening methods, such as X-rays and CT scans, have limitations in terms of accuracy and efficiency, leading to many cases of lung cancer being diagnosed at a later stage, when treatment options are limited. In this paper, we aim to develop a highly accurate and efficient tool for detecting lung cancer using a NN classifier. We first build a large dataset of medical images and patient data for training and evaluating the NN classifier. The dataset includes a variety of imaging modalities, including CT scans, X-rays, and other medical images. We then develop and train a NN classifier for lung cancer detection, using a deep learning technique. The NN classifiers optimized for high accuracy and efficiency, with the goal of achieving earlier and more accurate diagnosis of lung cancer. We evaluate the performance of the NN classifier using a variety of metrics, including sensitivity, specificity, and area under the receiver operating characteristic curve (AUC-ROC). The classifier is tested on a separate test dataset to ensure that it generalizes well to new data. We also compare the performance of the NN classifier to other traditional screening methods, such as X-rays and CT scans, to determine the potential impact of the NN classifier on lung cancer screening. Finally, we use explainable machine learning technique called as GLCM to identify specific features and patterns in medical images that are indicative of lung cancer. This analysis provides insights into other underlying mechanisms of lung cancer development and may lead to new discoveries and treatment options.
Key words: NN Classifier / Median filtering / DWT / non-small cell lung cancer (NSCLC) / Small Cell Lung Cancer (SCLC) / GLCM / receiver operating characteristic curve (AUC-ROC) / Computer Aided Diagnosis (CAD)
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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