@inproceedings{8ebd43268ecd425ea36cf3ecf0c2dec7,
title = "LCDctCNN: Lung Cancer Diagnosis of CT scan Images Using CNN Based Model",
abstract = "The most deadly and life-threatening disease in the world is lung cancer. Though early diagnosis and accurate treatment are necessary for lowering the lung cancer mortality rate. A computerized tomography (CT) scan-based image is one of the most effective imaging techniques for lung cancer detection using deep learning models. In this article, we proposed a deep learning modelbased Convolutional Neural Network (CNN) framework for the early detection of lung cancer using CT scan images. We also have analyzed other models for instance Inception V3, Xception, and ResNet-50 models to compare with our proposed model. We compared our models with each other considering the metrics of accuracy, Area Under Curve (AUC), recall, and loss. After evaluating the model's performance, we observed that CNN outperformed other models and has been shown to be promising compared to traditional methods. It achieved an accuracy of 92%, AUC of 98.21%, recall of 91.72%, and loss of 0.328.",
keywords = "CNN, CT scan imaging, Deep Learning, Inception V3, Lung cancer, ResNet-50, Xception",
author = "Muntasir Mamun and Mahmud, {Md Ishtyaq} and Mahabuba Meherin and Ahmed Abdelgawad",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 10th International Conference on Signal Processing and Integrated Networks, SPIN 2023 ; Conference date: 23-03-2023 Through 24-03-2023",
year = "2023",
doi = "10.1109/SPIN57001.2023.10116075",
language = "English",
series = "Proceedings of the 10th International Conference on Signal Processing and Integrated Networks, SPIN 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "205--212",
editor = "Pandey, {Manoj Kumar} and Rai, {J. K.} and Pradeep Kumar and Dubey, {Ashwani Kumar} and Shukla, {Anil Kumar}",
booktitle = "Proceedings of the 10th International Conference on Signal Processing and Integrated Networks, SPIN 2023",
}