Abstract
Lung cancer is a disease caused by abnormal lung cell growth. The number of people of all ages and sexes with lung tumors is constantly increasing. Classical classification of lung tumors can be sometimes misleading and time-consuming. Consequently, automated diagnosis is becoming a necessity to avoid the occurrence of errors and increase the survival rate of patients with lung tumors. However, deep learning and transfer learning are effective tools for the early detection and classification of lung tumors on the basis of anatomopathological slides of the lung. This work presents a Deep learning/Transfer learning implementation for lung tumor classification using a six-class database (LUAD, LUSC, SCLC, PTB, OP, NL). In order to reach the average accuracy of 98.9%, the implementation was trained using three convolutional neural network models: VGG19, ResNet50, and InceptionV3.
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Gasmi, M., Derdour, M., Gahmous, A. (2022). Transfer Learning for the Classification of Small-Cell and Non-small-Cell Lung Cancer. In: Bennour, A., Ensari, T., Kessentini, Y., Eom, S. (eds) Intelligent Systems and Pattern Recognition. ISPR 2022. Communications in Computer and Information Science, vol 1589. Springer, Cham. https://doi.org/10.1007/978-3-031-08277-1_28
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DOI: https://doi.org/10.1007/978-3-031-08277-1_28
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