Abstract
In this paper, we propose an automated deep learning Convolutional Neural Network (CNN) based detection method to distinguish cancerous tumors from benign tumors to identify pancreatic cancer in Computed Tomography (CT) images. We train and test the model using two datasets provided by The Cancer Imaging Archive (TCIA) and the Medical Segmentation Decathlon (MSD). The first dataset contains 18942 CT images of 82 patients with normal pancreas while the second dataset contains 15000 images of 280 patients with confirmed pancreatic cancer. Our proposed model is a modified extension of the current CNN network with the addition of the Densely Connected Convolutional layers. Our method has shown superior results compared to other research and deep learning method with an accuracy of 97.4%, sensitivity of 98.3%, and specificity of 96.6%.
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Acknowledgment
This research is funded by Hanoi University of Science and Technology (HUST) under project number T2021-PC-018.
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Huy, H.Q., Dat, N.T., Hiep, D.N., Tram, N.N., Vu, T.A., Huong, P.T.V. (2023). Pancreatic Cancer Detection Based on CT Images Using Deep Learning. In: Nguyen, T.D.L., Verdú, E., Le, A.N., Ganzha, M. (eds) Intelligent Systems and Networks. ICISN 2023. Lecture Notes in Networks and Systems, vol 752. Springer, Singapore. https://doi.org/10.1007/978-981-99-4725-6_10
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DOI: https://doi.org/10.1007/978-981-99-4725-6_10
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