Gastrointestinal tract disease recognition based on denoising capsule network

References

• Afriyie, Y., Weyori, B. A., & Opoku, A. A. (2021). Exploring optimised capsule network on complex images for medical diagnosis. 2–17. https://ieeexplore.ieee.org/abstract/document/9682081
   Google Scholar
• Akram, T., Attique, M., Muhammad, K., & Mussarat, S. (2018). Skin lesion segmentation and recognition using multichannel saliency estimation and m-svm on selected serially fused features. Journal of Ambient Intelligence and Humanized Computing, https://doi.org/10.1007/s12652-018-1051-5
   Google Scholar
• Asperti, A., & Mastronardo, ca. 2018. “The effectiveness of data augmentation for detection of gastrointestinal diseases from endoscopical images.” BIOIMAGING 2018 - 5th international conference on bioimaging, proceedings; part of 11th international joint conference on biomedical engineering systems and technologies, BIOSTEC 2018 2: 199–205. https://doi.org/10.5220/0006730901990205.
   Google Scholar
• Ayidzoe, M. A., Yongbin, Y., Kwabena, P., Jingye, M., Kwabena, C., & Yifan, A. (2021). Gabor capsule network with preprocessing blocks for the recognition of complex images. Machine Vision and Applications, 123. https://doi.org/10.1007/s00138-021-01221-6
   Web of Science ®Google Scholar
• Bretthauer, M. (2011). Colorectal Cancer Screening. Journal of Internal Medicine, 270(2), 87–98. https://doi.org/10.1111/j.1365-2796.2011.02399.x
   PubMed Web of Science ®Google Scholar
• Byrne, M. F., Chapados, N., Soudan, F., Oertel, C., Linares Pérez, M., Kelly, R., Iqbal, N., Chandelier, F., & Rex, D. K. (2019a). Real-time differentiation of adenomatous and hyperplastic diminutive colorectal polyps during analysis of unaltered videos of standard colonoscopy using a deep learning model. Gut, 68(1), 94–100. https://gut.bmj.com/content/68/1/94.abstract
   PubMed Web of Science ®Google Scholar
• Byrne, M. F., Chapados, N., Soudan, F., Oertel, C., Linares Pérez, M., Kelly, R., Iqbal, N., Chandelier, F., & Rex, D. K. (2019b). Real-time differentiation of adenomatous and hyperplastic diminutive colorectal polyps during analysis of unaltered videos of standard colonoscopy using a deep learning model. Gut. https://doi.org/10.1136/gutjnl-2017-314547
   Google Scholar
• Campus, W., Road, M., & Engineering, C. (2018). Automated Ulcer and Bleeding Classification from Wce Images Using Multiple Features Fusion and Selection. 18(4), 1–25. https://doi.org/10.1142/S0219519418500380
   Google Scholar
• Chang, Y., & Chen, W. (2019). Gastrointestinal tract diseases detection with deep attention neural network. 2568–2572. https://doi.org/10.1145/3343031.3356061
   Google Scholar
• Chang, S., & Liu, J. I. N. (2020). Multi-lane capsule network for classifying images with complex background. IEEE Access. https://doi.org/10.1109/ACCESS.2020.2990700
   Google Scholar
• Gao, B., & Pavel, L. (n.d.). On the properties of the softmax function with application in game theory and reinforcement learning. 1–10. https://arxiv.org/abs/1704.00805
   Google Scholar
• Ghatwary, N., Ye, X., & Zolgharni, M. (2019). Esophageal abnormality detection using densenet based faster R-CNN With Gabor Features. IEEE Access, 7, 84374–84385. https://doi.org/10.1109/ACCESS.2019.2925585
   Web of Science ®Google Scholar
• Hirasawa, T., Aoyama, K., Tanimoto, T., Ishihara, S., Shichijo, S., Ozawa, T., Ohnishi, T., Fujishiro, M., Matsuo, K., Fujisaki, J., & Tada, T. (2018). Application of artificial intelligence using a convolutional neural network for detecting gastric cancer in endoscopic images. Gastric Cancer, 21(4), 653–660. https://doi.org/10.1007/s10120-018-0793-2
   PubMed Web of Science ®Google Scholar
• Itoh, T., Kawahira, H., Nakashima, H., Yata, N., Kawahira, H., & Medical Engineering, F. (2018). Deep learning analyzes helicobacter pylori infection by upper gastrointestinal endoscopy images. 4–9. https://doi.org/10.1055/s-0043-120830
   Google Scholar
• JangHyung, L., Young Jae, K., Yoon Woo, K., Sungjin, P., Youn-i, C., Yoon Jae, K., Dong Kyun, P., Kwang Gi, K., & Jun-Won, C. (2019). Spotting MALIGNANCIES FROM GASTRIC ENDOSCOPIC IMAGES USING DEEP LEARNING. Surgical Endoscopy. https://doi.org/10.1007/s00464-019-06677-2
   Google Scholar
• Japkowicz, N., & Shah, M. (2011). Evaluating learning algorithms: a classification perspective. Cambridge University Press. https://doi.org/10.5555/1964882
   Google Scholar
• Kahsaygebreslassie, A., Yaecobgirmaygezahegn, Tsighe Hagos, M., Achimibenthal, & Pooja, 2019. “Automated gastrointestinal disease recognition for endoscopic images.” Proceedings - 2019 International Conference on Computing, Communication, and Intelligent Systems, ICCCIS 2019. January 312–316. https://doi.org/10.1109/ICCCIS48478.2019.8974458.
   Google Scholar
• Khan, M. A., Akram, T., Sharif, M., Ahmad, S., & Bukhari, C. (2020). An integrated framework of skin lesion detection and recognition through saliency method and optimal deep neural network features selection. 15929–15948. https://doi.org/10.1007/s00521-019-04514-0
   Google Scholar
• Khan, M. A., Lali, I. U., Rehman, A., Ishaq, M., Sharif, M., & Saba, T. (2019). “brain tumor detection and classification : a framework of marker-based watershed algorithm and Multilevel priority features selection,” no. October, 909–922. https://doi.org/10.1002/jemt.23238
   Google Scholar
• Khan, M. A., Rubab, S., Kashif, A., Imran, M., Muhammad, N., Hussain, J., Zhang, Y.-D., & Chandra, S. (2020). Lungs cancer classification from CT Images : An integrated design of contrast based classical features fusion and selection. Pattern Recognition Letters, 129, 77–85.
   Web of Science ®Google Scholar
• Khan, M. A., Sharif, M., Akram, T., Ahmad Chan Bukhari, S., & Sunder Nayak, R. (2020). Developed newton-raphson based deep features selection framework for skin lesion recognition. Pattern Recognition Letters, 129, 293–303. https://doi.org/10.1016/j.patrec.2019.11.034
   Web of Science ®Google Scholar
• Khan, M. A., Sharif, M., Akram, T., Yasmin, M., & Sunder Nayak, R. (2019). Stomach deformities recognition using rank-based deep features selection. Journal of Medical Systems, 43(12). https://doi.org/10.1007/s10916-019-1466-3
   Google Scholar
• Komeda, Y., Handa, H., Watanabe, T., Nomura, T., Kitahashi, M., Sakurai, T., Okamoto, A. (2017). Computer-aided diagnosis based on convolutional neural network system for colorectal polyp classification: preliminary experience. Oncology (Switzerland), 93(1), 30–34. https://doi.org/10.1159/000481227
   PubMedGoogle Scholar
• Krizhevsky, A. 2009. “Learning multiple layers of features from tiny images. http://www.cs.utoronto.ca/~kriz/learning-features-2009-TR.pdf
   Google Scholar
• Ling, C. X., Huang, J., & Zhang, H. (2003). AUC: A better measure than accuracy in comparing learning algorithms. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2671, 329–341. https://doi.org/10.1007/3-540-44886-1_25
   Web of Science ®Google Scholar
• Maeda, Y., Kudo, S.-E., Mori, Y., Misawa, M., Ogata, N., Sasanuma, S., Wakamura, K., Oda, M., Mori, K., & Ohtsuka, K. (2019). Fully automated diagnostic system with arti fi cial intelligence using endocytoscopy to identify the presence of histologic in fl ammation associated with ulcerative colitis (with Video). Gastrointestinal Endoscopy, 89(2), 408–415. https://doi.org/10.1016/j.gie.2018.09.024
   PubMed Web of Science ®Google Scholar
• Matthews, B. W. (1975). Comparison of the Predicted and Observed Secondary Structure of T4 Phage Lysozyme. BBA - Protein Structure, 405(2), 442–451. https://doi.org/10.1016/0005-2795(75)90109-9
   PubMed Web of Science ®Google Scholar
• Mensah, P., Asubam, B., & Abra, A. (2020). Exploring the Performance of LBP-capsule networks with k-means routing on complex images. In Journal of King Saud University - Computer and Information Sciences (pp. 2574–2588). https://doi.org/10.1016/j.jksuci.2020.10.006
   Google Scholar
• Mossotto, E., Ashton, J. J., Coelho, T., Beattie, R. M., Macarthur, B. D., & Ennis, S. (2017). Classification of paediatric inflammatory bowel disease using machine learning. Scientific reports. https://doi.org/10.1038/s41598-017-02606-2
   Google Scholar
• Nasir, M., Attique, M., Ullah, I., Tanzila, L., Tassawar, S., & Sharif, M. (2018). An improved strategy for skin lesion detection and classification using uniform segmentation and feature selection based approach. no. December 2017. Microscopy research and technique. https://doi.org/10.1002/jemt.23009
   Google Scholar
• Orchard, J., Ebrahimi, M., Wong, A., & Canada, N. (2008). Efficient nonlocal-means denoising using the SVD University of waterloo Cheriton school of computer science, 2 Dept . of Applied Mathematics, 3 Systems Design Engineering Dept . Design Engineering, 1732–1735. https://ieeexplore.ieee.org/abstract/document/4712109
   Google Scholar
• Ozawa, T., Ishihara, S., & Fujishiro, M. (2018) Novel Computer-Assisted Diagnosis System for Endoscopic Disease Activity in Patients with Ulcerative Colitis. Gastrointestinal Endoscopy. https://doi.org/10.1016/j.gie.2018.10.020
   Google Scholar
• Pogorelov, K., De Lange, T., Johansen, D., Thelin Schmidt, P., Riegler, M., Lux, M., & Halvorsen, P. (n.d.). Kvasir : A multi-class image dataset for computer aided gastrointestinal disease detection. https://dl.acm.org/doi/abs/10.1145/3083187.3083212
   Google Scholar
• Poudel, S., & Woong Lee, S. (2021). Deep Multi-Scale Attentional features for medical image segmentation. Applied Soft Computing, 109, 107445. https://doi.org/10.1016/j.asoc.2021.107445
   Web of Science ®Google Scholar
• Provost, F., Fawcett, T., & Kohavi, R. 1998. “The case against accuracy estimation for comparing induction algorithms.” International Conferenceon Machine Learning, 445.
   Google Scholar
• Sara, S., Frosst, N., & Hinton, G. E. (2017, December). Dynamic Routing between Capsules. Advances in Neural Information Processing Systems, Nips, 3857–3867. https://proceedings.neurips.cc/paper/2017/file/2cad8fa47bbef282badbb8de5374b894-Paper.pdf
   Google Scholar
• Sharif, M., Attique Khan, M., Rashid, M., Yasmin, M., Afza, F., John Tanik, U., Sharif, M., Attique Khan, M., & Rashid, M. (2021). Deep CNN and Geometric Features-Based Gastrointestinal Tract Diseases Detection and Classification from Wireless Capsule Endoscopy Images. Journal of Experimental & Theoretical Artificial Intelligence, 33(4), 577–600. https://doi.org/10.1080/0952813X.2019.1572657
   Web of Science ®Google Scholar
• Sharif, M. I., Jian Ping, L., Attique Khan, M., & Asim Saleem, M. (2019) Active Deep Neural Network Features Selection for Segmentation and Recognition of Brain Tumors Using MRI Images. Pattern Recognition Letters. https://doi.org/10.1016/j.patrec.2019.11.019
   Google Scholar
• Shees, N., Atif Tahir B, M., Sadiq, S., Naqvi, A., & Zaid, M. (2018). Ensemble of texture and deep learning features for finding abnormalities in the gastro-intestinal tract. Springer International Publishing. https://doi.org/10.1007/978-3-319-98446-9
   Google Scholar
• Shichijo, S., Nomura, S., Aoyama, K., Nishikawa, Y., Miura, M., Shinagawa, T., Takiyama, H., Tanimoto, T., Ishihara, S., Matsuo, K., & Tada, T. (2017). Application of convolutional neural networks in the diagnosis of Helicobacter Pylori infection based on endoscopic images. EBioMedicine, 25, 106–111. https://doi.org/10.1016/j.ebiom.2017.10.014
   PubMed Web of Science ®Google Scholar
• Siegel, R. L., Fedewa, S. A., Anderson, W. F., Miller, K. D., Jiemin, M., Rosenberg, P. S., & Jemal, A. (2017). Colorectal Cancer Incidence Patterns in the United States, 1974-2013. Journal of the National Cancer Institute, 109(8), 27–32. https://doi.org/10.1093/jnci/djw322
   Google Scholar
• Singla, P., & Domingos, P. 2005. “Discriminative training of Markov logic networks.” Proceedings of the National Conference on Artificial Intelligence 2: 868–873.
   Google Scholar
• Sokolova, M. (n.d.). Beyond Accuracy, F-Score and ROC : A family of discriminant measures for performance evaluation. https://doi.org/10.1007/11941439_114
   Google Scholar
• Takiyama, H., Ozawa, T., Ishihara, S., Fujishiro, M., de Alcantara, B. N., Araujo, F. M. C., Queiroz, M. G. L., Cruz, A. C. R., Vasconcelos, B. H. B., Chiang, J. O., Martins, L. C., Casseb, L. M. N., da Silva, E. V., Carvalho, V. L., Vasconcelos, B. C. B., Rodrigues, S. G., Oliveira, C. S., Quaresma, J. A. S., & Vasconcelos, P. F. C. (2018). Automatic Anatomical Classification of Esophagogastroduodenoscopy Images Using Deep Convolutional Neural Networks. Scientific Reports, 8(April), 1–8. https://doi.org/10.1038/s41598-018-25842-6
   PubMedGoogle Scholar
• Taruna, A., Rahul, G., & Shrikanth, N. 2019. “ON EVALUATING CNN REPRESENTATIONS FOR LOW RESOURCE MEDICAL IMAGE CLASSIFICATION Taruna Agrawal, Rahul Gupta, Shrikanth Narayanan Signal Analysis and Interpretation Lab, University of Southern California.” ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1363–1367.
   Google Scholar
• Vokes, E. E., Weichselbaum, R. R., Lippman, S. M., & Ki Hong, W. (1993). The New England journal of medicine . no other uses without permission. copyright © 1993 Massachusetts medical society. all rights reserved. The New England Journal of Medicine, 29(328 (17), 1230–1235. https://doi.org/10.1056/NEJM195205222462101
   Google Scholar
• Vollgraf, R. (n.d.). Fashion-MNIST : A novel image dataset for benchmarking machine learning algorithms. 1–6. https://arxiv.org/abs/1708.07747
   Google Scholar
• Wimmer, G., Vécsei, A., Häfner, M., & Uhl, A. (2018). Fisher Encoding of Convolutional Neural Network Features for Endoscopic Image Classification. Journal of Medical Imaging, 5(3), 1. https://doi.org/10.1117/1.jmi.5.3.034504
   Web of Science ®Google Scholar
• Xiong, Y., Guiping, S., Shiwe, Y., Sun, Y., & Sun, Y. (2019) Deeper Capsule Network For Complex Data. July, 1–8. https://ieeexplore.ieee.org/iel7/8840768/8851681/08852020.pdf
   Google Scholar
• Yuan, Y., Baopu, L., & Meng, M. Q. (2016). Improved bag of feature for automatic polyp detection in wireless capsule endoscopy images. 13(2), 529–535. https://ieeexplore.ieee.org/iel7/8856/7447676/07052426.pdf
   Google Scholar
• Yuan, Y., & Meng, M. Q. (2014). Polyp classification based on bag of features and saliency in wireless capsule endoscopy *. 3930–3935. https://ieeexplore.ieee.org/abstract/document/6907429
   Google Scholar
• Zhang, X., Chen, F., Yu Id, T., Jiye, A., Huang, Z., Jiquan Liu, I., Weiling, H., Wang, L., Duan, H., & Jianmin, S. (2019). Real-time gastric polyp detection using convolutional neural networks. 1–16. https://doi.org/10.1371/journal.pone.0214133
   Google Scholar
• Zhang, R., Zheng, Y., Wing Chung Mak, T., Ruoxi, Y., Wong, S. H., Lau, J. Y. W., & Poon, C. C. Y. (2017). Automatic detection and classification of colorectal polyps by transferring low-level CNN features from nonmedical domain. IEEE Journal of Biomedical and Health Informatics, 21(1), 41–47. https://ieeexplore.ieee.org/abstract/document/7769237
   PubMed Web of Science ®Google Scholar