Abstract
Lung cancer is the most prevalent malignancy that cannot be avoided and that causes late health care death. At now, CT scans can be used to assist physicians to diagnose early stage lung cancer. In many situations, lung cancer detection depends on doctors’ experience, which might neglect some patients and create certain issues. Deep learning in several diagnostic fields of medical imaging has become a popular and powerful approach. The deep study models employ the Convolutional Neural Network (CNN), which extracts features and classifies the picture using a fully connected network. The CNN leverages this functionality. The chapter presented study of deep learning algorithm for lung cancer detection. The experiment is performed with CNN by utilizing LIDC-IDRI dataset. It is referred to as the Lung Image Database Consortium image collection and comprises of diagnosis thoracic computed tomography (CT) scans which are labeled. It is accessible worldwide and is updated on a regular basis. The classification performance is measured for the matrices F1 score, Recall, precision, support, and accuracy. The accuracy achieved with experiment is 96.5%
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Tandon, R., Agrawal, S., Raghuwanshi, R., Rathore, N.P.S., Prasad, L., Jain, V. (2022). Automatic Lung Carcinoma Identification and Classification in CT Images Using CNN Deep Learning Model. In: Mishra, S., Tripathy, H.K., Mallick, P., Shaalan, K. (eds) Augmented Intelligence in Healthcare: A Pragmatic and Integrated Analysis. Studies in Computational Intelligence, vol 1024. Springer, Singapore. https://doi.org/10.1007/978-981-19-1076-0_9
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