Lung cancer classification with Convolutional Neural Network Architectures
One of the most common malignant tumors in the world today is lung cancer, and it is the primary cause of death from cancer. With the continuous advancement of urbanization and industrialization, the problem of air pollution has become more and more serious. The best treatment period for lung cance...
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Qubahan
2021-02-01
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| Series: | Qubahan Academic Journal |
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| Online Access: | https://journal.qubahan.com/index.php/qaj/article/view/33 |
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| author | Shivan H. M. Mohammed Ahmet Çinar |
| author_facet | Shivan H. M. Mohammed Ahmet Çinar |
| author_sort | Shivan H. M. Mohammed |
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One of the most common malignant tumors in the world today is lung cancer, and it is the primary cause of death from cancer. With the continuous advancement of urbanization and industrialization, the problem of air pollution has become more and more serious. The best treatment period for lung cancer is the early stage. However, the early stage of lung cancer often does not have any clinical symptoms and is difficult to be found. In this paper, lung nodule classification has been performed; the data have used of CT image is SPIE AAPM-Lung. In recent years, deep learning (DL) was a popular approach to the classification process. One of the DL approaches that have used is Transfer Learning (TL) to eliminate training costs from scratch and to train for deep learning with small training data. Nowadays, researchers have been trying various deep learning techniques to improve the efficiency of CAD (computer-aided system) with computed tomography in lung cancer screening. In this work, we implemented pre-trained CNN include: AlexNet, ResNet18, Googlenet, and ResNet50 models. These networks are used for training the network and CT image classification. CNN and TL are used to achieve high performance resulting and specify lung cancer detection on CT images. The evaluation of models is calculated by some matrices such as confusion matrix, precision, recall, specificity, and f1-score.
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| format | Article |
| id | doaj-art-3c8bd43391d94a7e9a4c4e5f2aaf457c |
| institution | Kabale University |
| issn | 2709-8206 |
| language | English |
| publishDate | 2021-02-01 |
| publisher | Qubahan |
| record_format | Article |
| series | Qubahan Academic Journal |
| spelling | doaj-art-3c8bd43391d94a7e9a4c4e5f2aaf457c2025-02-03T10:12:58ZengQubahanQubahan Academic Journal2709-82062021-02-011110.48161/qaj.v1n1a3333Lung cancer classification with Convolutional Neural Network ArchitecturesShivan H. M. Mohammed0Ahmet Çinar1Department of Computer Science, Duhok University, Duhok, IraqDepartment of Computer Engineering, Firat University, Elazig, Turkey One of the most common malignant tumors in the world today is lung cancer, and it is the primary cause of death from cancer. With the continuous advancement of urbanization and industrialization, the problem of air pollution has become more and more serious. The best treatment period for lung cancer is the early stage. However, the early stage of lung cancer often does not have any clinical symptoms and is difficult to be found. In this paper, lung nodule classification has been performed; the data have used of CT image is SPIE AAPM-Lung. In recent years, deep learning (DL) was a popular approach to the classification process. One of the DL approaches that have used is Transfer Learning (TL) to eliminate training costs from scratch and to train for deep learning with small training data. Nowadays, researchers have been trying various deep learning techniques to improve the efficiency of CAD (computer-aided system) with computed tomography in lung cancer screening. In this work, we implemented pre-trained CNN include: AlexNet, ResNet18, Googlenet, and ResNet50 models. These networks are used for training the network and CT image classification. CNN and TL are used to achieve high performance resulting and specify lung cancer detection on CT images. The evaluation of models is calculated by some matrices such as confusion matrix, precision, recall, specificity, and f1-score. https://journal.qubahan.com/index.php/qaj/article/view/33Deep learning; Transfer learning; Lung cancer; pre-trained network. |
| spellingShingle | Shivan H. M. Mohammed Ahmet Çinar Lung cancer classification with Convolutional Neural Network Architectures Qubahan Academic Journal Deep learning; Transfer learning; Lung cancer; pre-trained network. |
| title | Lung cancer classification with Convolutional Neural Network Architectures |
| title_full | Lung cancer classification with Convolutional Neural Network Architectures |
| title_fullStr | Lung cancer classification with Convolutional Neural Network Architectures |
| title_full_unstemmed | Lung cancer classification with Convolutional Neural Network Architectures |
| title_short | Lung cancer classification with Convolutional Neural Network Architectures |
| title_sort | lung cancer classification with convolutional neural network architectures |
| topic | Deep learning; Transfer learning; Lung cancer; pre-trained network. |
| url | https://journal.qubahan.com/index.php/qaj/article/view/33 |
| work_keys_str_mv | AT shivanhmmohammed lungcancerclassificationwithconvolutionalneuralnetworkarchitectures AT ahmetcinar lungcancerclassificationwithconvolutionalneuralnetworkarchitectures |