![Open Access](https://i-scholar.in/lib/pkp/templates/images/icons/fulltextgreen.png)
![Restricted Access](https://i-scholar.in/lib/pkp/templates/images/icons/fulltextred.png)
![Open Access](https://i-scholar.in/lib/pkp/templates/images/icons/fulltextgreen.png)
![Open Access](https://i-scholar.in/lib/pkp/templates/images/icons/fulltext_open_medium.gif)
![Restricted Access](https://i-scholar.in/lib/pkp/templates/images/icons/fulltextred.png)
![Restricted Access](https://i-scholar.in/lib/pkp/templates/images/icons/fulltext_restricted_medium.gif)
Detection Of Liver Cancer From CT Images Using CapsNet
Subscribe/Renew Journal
Primary adult liver cancer is currently classified into two main diagnostic categories: cholangiocarcinoma and hepatocellular carcinoma. Hepatocellular carcinoma is the most common kind of liver cancer in adults. These are extremely heterogeneous tumours with a wide range of morphological and clinical characteristics, which reflects the wide range of oncological drugs available and the intricate pathways that lead to carcinogenesis. Because of the large quantity of data acquired from phenotypic and molecular research, the classification of liver cancer is shifting away from the old method, which is based on morphological aspects, and toward a more functional approach based on functional characteristics. In this paper, we develop a Capsule Network (CapsNet) classifier to classify the liver regions from computerized tomography (CT) images. The CapsNet helps in classification of instances using pre-processing and feature extraction stages. The training of the classifier is conducted using various liver images from the input datasets and the classifier is validated using the test images. The simulation is conducted to test the effectiveness of CapsNet and the results of simulation shows that the proposed method achieves higher degree of classification than other methods.
Keywords
Medical Images, Deep Learning, CapsNet, Image Processing
Subscription
Login to verify subscription
User
Font Size
Information
- S. Karthick, P.A. Rajakumari and R.A. Raja, “Ensemble Similarity Clustering Frame work for Categorical Dataset Clustering using Swarm Intelligence”, Proceedings of International Conference on Intelligent Computing and Applications, pp. 549-557, 2021.
- A. Khadidos, A.O. Khadidos, S. Kannan and G. Tsaramirsis, “Analysis of COVID-19 Infections on a CT Image using Deep Sense Model”, Frontiers in Public Health, Vol. 8, pp. 1-20, 2020.
- E. Goceri, “CapsNet Topology to Classify Tumours from Brain Images and Comparative Evaluation”, IET Image Processing, Vol. 14, No. 5, pp. 882-889, 2020.
- V. Maheshwari, M.R. Mahmood and S. Sravanthi, “Nanotechnology-Based Sensitive Biosensors for COVID19 Prediction using Fuzzy Logic Control”, Journal of Nanomaterials, Vol. 2021, pp. 1-13, 2021.
- K. Pragash and T. Karthikeyan, “Data Privacy Preservation and Trade-off Balance between Privacy and Utility using Deep Adaptive Clustering and Elliptic Curve Digital Signature Algorithm”, Wireless Personal Communications, pp. 1-16, 2021.
- J.P. Vigueras-Guillen, A. Patra and F. Seeliger, “Parallel Capsule Networks for Classification of White Blood Cells”, Proceedings of International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 743752, 2021.
- K. Srihari, G. Dhiman, S. Chandragandhi and H.F. Alharbi, “An IoT and Machine Learning‐based Routing Protocol for Reconfigurable Engineering Application”, IET Communications, Vol. 23, pp. 1-19, 2021.
- A. Hoogi, B. Wilcox, Y. Gupta and D.L. Rubin, “SelfAttention Capsule Networks for Image Classification”, Proceedings of International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 451456, 2021.
- T. Nguyen, B.S. Hua and N. Le, “3D-UCaps: 3D Capsules Unet for Volumetric Image Segmentation”, Proceedings of International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 548-558, 2021.
- P. Afshar, A. Mohammadi and K.N. Plataniotis, “From Handcrafted to Deep-Learning-based Cancer Radiomics: Challenges and Opportunities”, IEEE Signal Processing Magazine, Vol. 36, No. 4, pp. 132-160, 2019.
- C. Pino, G. Vecchio, M. Fronda and C. Spampinato, “TwinLiverNet: Predicting TACE Treatment Outcome from CT scans for Hepatocellular Carcinoma using Deep Capsule Networks”, Proceedings of Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 3039-3043, 2021.
- N. Noreen, S. Palaniappan and A. Qayyum, “A Deep Learning Model based on Concatenation Approach for the Diagnosis of Brain Tumor”, IEEE Access, Vol. 8, pp. 5513555144, 2020.
- Y. Tan, J. Qin and L. Huang, “Recent Progress of Medical CT Image Processing Based on Deep Learning”, Proceedings of International Conference on Artificial Intelligence and Security, pp. 418-428, 2021.
- A. Mobiny, P. Yuan and P.A. Cicalese, “Memory Augmented Capsule Network for Adaptable Lung Nodule Classification”, IEEE Transactions on Medical Imaging, Vol. 34, No. 2, pp. 1-14, 2021.
- R.F. Mansour and S. Kumar, “Unsupervised Deep Learning based Variational Autoencoder Model for COVID-19 Diagnosis and Classification”, Pattern Recognition Letters, Vol. 151, pp. 267-274, 2021.
- F. Ozyurt and E. Dogantekin, “Brain Tumor Detection based on Convolutional Neural Network with Neutrosophic Expert Maximum Fuzzy Sure Entropy”, Measurement, Vol. 147, pp. 106830-106843, 2019.
![](https://i-scholar.in/public/site/images/abstractview.png)
Abstract Views: 138
![](https://i-scholar.in/public/site/images/pdfview.png)
PDF Views: 0