Abstract
One of the biggest difficulties facing medicine today is cancer. Lung cancer along with colon cancer, stomach cancer and liver cancer, are the four most dangerous cancers. If the disease is detected early and treated properly, it can prolong the patient’s life. Today, many tasks in many fields, including medicine, can be resolved by using deep learning techniques. This paper proposes to modify the Deep Neural Network transfer learning for the lung and colon cancer classification based on GoogLeNet. Specifically, the main idea of the inception module of GoogLeNet that is running multiple operations (pooling, convolution) with multiple filter sizes in parallel so that we do not have to face any trade-off. The second advantage of the inception module is dimensionality reduction of feature maps and over parameterization dealing. The output of classification was adjusted to 3 or 2 classes due to the required classes of lung and colon problems. The accuracy of the proposed method is 99.66% and 100% in the lung and colon image dataset, respectively. The results of the proposed method are better than the other methods such as VGG16, Resnet50, NASNetMobile and original GoogLeNet.
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We acknowledge the support of time and facilities from Ho Chi Minh City University of Technology (HCMUT), VNU-HCM and Ho Chi Minh city University of Education for this study.
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Hoang, T.H., Binh, N.T., Van, V., Tan, N.Q. (2022). Lung and Colon Tumor Classification Based on Transfer Learning-Based Techniques. In: Dang, T.K., Küng, J., Chung, T.M. (eds) Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications. FDSE 2022. Communications in Computer and Information Science, vol 1688. Springer, Singapore. https://doi.org/10.1007/978-981-19-8069-5_42
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DOI: https://doi.org/10.1007/978-981-19-8069-5_42
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