Abstract
Lung cancer is one of the leading contributing factors to the mortality rate. The prevailing types of non-small cell lung cancer (NSCLC) include adenocarcinoma, large cell carcinoma, and squamous cell carcinoma. Studies have shown that 18% of the mortality rate stems from this disease. This is attributable to substandard diagnosis techniques and inefficient treatments available to cure metastasis. Hence, this paper opts to employ transfer learning techniques by using different state-of-the-art, pre-trained models to detect lung cancer and classify it into four groups, namely adenocarcinoma, large cell carcinoma, squamous cell carcinoma, and normal using chest CT scan images, followed by conducting a comparative analysis of their performances. The models implemented are ResNet101, VGG16, InceptionV3 and DenseNet169. Moreover, the paper proposes a new CNN model using the ensemble technique which is an amalgamation of ResNet101 and InceptionV3 that are employed initially. In addition to that, it introduces another 11 layered CNN model built from the outset. The dataset used for this study is called chest CT scan images which is retrieved from Kaggle. The models’ performances are analyzed utilizing different metrics like accuracy, precision, recall, F1-score, and AUC. The ensemble of ResNet101 and InceptionV3 models has achieved the highest accuracy of 93.7%, on this particular dataset, based on which a Web app is deployed to demonstrate real-world application.
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Akhand, ZEN. et al. (2023). Lung Cancer Detection Using Ensemble Technique of CNN. In: Ahmad, M., Uddin, M.S., Jang, Y.M. (eds) Proceedings of International Conference on Information and Communication Technology for Development. Studies in Autonomic, Data-driven and Industrial Computing. Springer, Singapore. https://doi.org/10.1007/978-981-19-7528-8_39
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DOI: https://doi.org/10.1007/978-981-19-7528-8_39
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