Abstract
Computed tomography (CT) is critical for identifying tumors and detecting lung cancer. As was the case in the recent past, we wish to incorporate a well-educated, profound learning algorithm to recognize and categorize lung nodules based on clinical CT imagery. This investigation used open-source datasets and data from multiple centers. Deep learning is a widely used and powerful technique for pattern recognition and categorization. However, because large datasets of medical images are not always accessible, there are few deep structured applications used in diagnostic medical imaging. In this research, a deep learning model was created to identify lung tumors from histopathological images. Our proposed Deep Learning (DL) model accuracy was 95% and loss was 0.158073%.
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Mohalder, R.D., Hossain, K.A., Sarkar, J.P., Paul, L., Raihan, M., Talukder, K.H. (2023). Lung Cancer Detection from Histopathological Images Using Deep Learning. In: Satu, M.S., Moni, M.A., Kaiser, M.S., Arefin, M.S. (eds) Machine Intelligence and Emerging Technologies. MIET 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 490. Springer, Cham. https://doi.org/10.1007/978-3-031-34619-4_17
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