Abstract
Lung cancer is becoming one of the most menacing cancers to human health. Small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC) are the two kinds of lung cancer, which are classified based on patterns in behavior and treatment response. Non-small cell lung cancer is classified as lung adenocarcinomas, lung squamous cell carcinomas, and large cell carcinoma. The two most prevalent types of NSCLC are lung adenocarcinoma, which accounts for about 40% and lung squamous cell carcinoma (LUSC), which accounts for almost 25–30% of all lung cancers. Building an automated categorization system for these two primary NSCLC subtypes is vital for building a computer-aided diagnostic system (CAD). CAD can improve the quality and efficiency of medical image analysis by increasing diagnosis accuracy and stability, reducing the chance of wrong diagnosis due to subjective factors and missed diagnosis. With the rapid development of Convolutional Neural Networks (CNN) in image processing, a variety of CNN architectures have emerged, that achieve outstanding image classification performance. In this paper InceptionV3, DenseNet-201, and XceptionNet are selected as candidate networks due to their outstanding classification performance with 99.07%, 95.63%, 98.9%, respectively. The results of our study shows that InceptionV3 performs well in the categorization of types of non-small cell lung cancer histopathological images and benign images.
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Mothkur, R., Veerappa, B.N. (2023). Deep Learning-Based Three Type Classifier Model for Non-small Cell Lung Cancer from Histopathological Images. In: Kumar, S., Sharma, H., Balachandran, K., Kim, J.H., Bansal, J.C. (eds) Third Congress on Intelligent Systems. CIS 2022. Lecture Notes in Networks and Systems, vol 613. Springer, Singapore. https://doi.org/10.1007/978-981-19-9379-4_35
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DOI: https://doi.org/10.1007/978-981-19-9379-4_35
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