Identification of Polyp from Colonoscopy Images by Deep Belief Network based Polyp Detector Integration Model
Keywords:DBN, Deep Residual Network, Resnet-50, Polyp Detector, Two Stage Model, Polyp Network
Cancer is a disease involving unusual cell growth likely to spread to other parts of the body. According to WHO 2020 report, colorectal malignancy is the globally accepted second leading cause of cancer related deaths. Colorectal malignancy arises when malignant cells often called polyp, grow inside the tissues of the colon or rectum of the large intestine. Colonoscopy, CT scan, Histopathological analysis are some manual approaches of malignancy detection that are time consuming and lead to diagnostic errors. Supervised CNN data model requires a large number of labeled training samples to learn parameters from images. In this study we propose an expert system that can detect the colorectal malignancy and identify the exact polyp area from complex images. In this approach an unsupervised Deep Belief Network (DBN) is applied for effective feature extraction and classification of images. The classified image output of DBN is utilized by Polyp Detector. Residual network and feature extractor components of Polyp Detector helps polyp inspector in pixel wise learning. Two stage polyp network (PLPNet) is a R-CNN architecture with two stage advantage. The first stage is the extension of R-CNN to detect the polyp lesion area through a location box also called Polyp Inspector. Second Stage performs polyp segmentation. Polyp Inspector transfers the learned semantics to the polyp segmentation stage. It helps to enhance the ability to detect polyp with improved accuracy and guide the learning process. Skip schemes enrich the feature scale. Publicly available CVC-Clinical DB and CVC Colon DB datasets are used for experiment purposes to achieve a better prediction capability for clinical practices.
Chen, S., Urban, G., & Baldi, P. (2022). Weakly Supervised Polyp Segmentation in Colonoscopy Images Using Deep Neural Networks. Journal of Imaging, 8(5), 121. DOI: https://doi.org/10.3390/jimaging8050121
Yue, G., Han, W., Li, S., Zhou, T., Lv, J., & Wang, T. (2022). Automated polyp segmentation in colonoscopy images via deep network with lesion-aware feature selection and refinement. Biomedical Signal Processing and Control, 78, 103846. DOI: https://doi.org/10.1016/j.bspc.2022.103846
Dash, S., Padhy, S., Azad, S.M.A.K., Nayak, M. (2023). Intelligent IoT-Based Healthcare System Using Blockchain. Ambient Intelligence in Health Care. Smart Innovation, Systems and Technologies, vol 317. Springer, Singapore. https://doi.org/10.1007/978-981- 19- 6068-0_30
Padhy, Sasmita, Majed Alowaidi, Sachikanta Dash, Mohamed Alshehri, Prince Priya Malla, Sidheswar Routray, and Hesham Alhumyani. 2023. "AgriSecure: A Fog Computing- Based Security Framework for Agriculture 4.0 via Blockchain" Processes 11, no. 3: 757. https://doi.org/10.3390/pr11030757(SCIE-IF-3.352) DOI: https://doi.org/10.3390/pr11030757
Das, S. K., Pani, S. K., Padhy, S., Dash, S., & Acharya, A. K. (2023). Application of Machine Learning Models for Slope Instabilities Prediction in Open Cast mines. International Journal of Intelligent Systems and Applications in Engineering, 11(1), 111-121. (Scopus)
Dash, S., Padhy, S., Parija, B., Rojashree, T., & Patro, K. A. K. (2022). A Simple and Fast Medical Image Encryption System Using Chaos-Based Shifting Techniques. International Journal of Information Security and Privacy (IJISP), 16(1), 1-24 DOI: https://doi.org/10.4018/IJISP.303669
Panda, R., Dash, S., Padhy, S., Das, R.K. (2023). Diabetes Mellitus Prediction Through Interactive Machine Learning Approaches. In: Kumar, R., Pattnaik, P.K., R. S. Tavares,J.M. (eds) Next Generation of Internet of Things. Lecture Notes in Networks and Systems, vol 445. Springer, Singapore. https://doi.org/10.1007/978-981-19-1412-6_12. DOI: https://doi.org/10.1007/978-981-19-1412-6_12
Sameer Kumar Das, Subhendu Kumar Pani, Abhaya Kumar Samal, Sasmita Padhy, Sachikanta Dash and Singam Jayanthu, “Improving Slope Stability in Open Cast Mines via Machine Learning based IoT Framework ”International Journal of Advanced Computer Science and Applications (IJACSA) ,13(10),2022. http://dx.doi.org/10.14569/IJACSA.2022.01310115 DOI: https://doi.org/10.14569/IJACSA.2022.01310115
Padhy, S., Shankar, T. N., & Dash, S. (2022). A Comparison among Fast Point Multiplication Algorithms in Elliptic Curve Cryptosystem. DOI: https://doi.org/10.21203/rs.3.rs-862241/v1
Das, M., Swain, D., & Paikaray, B. K. (2020). Privacy Preservation in ROI of Medical Images Using LSB Manipulation. In Machine Learning and Information Processing: Proceedings of ICMLIP 2019 (pp. 245-253). Springer Singapore. DOI: https://doi.org/10.1007/978-981-15-1884-3_23
T. N. Shankar, S. Padhy, S. Dash, M. B. Teja and S. Yashwant, "Induction of Secure Data Repository in Blockchain over IPFS," 2022 6th International Conference on Trends in Electronics and Informatics (ICOEI), 2022, pp. 738-743, doi: 10.1109/ICOEI53556.2022.9776967. DOI: https://doi.org/10.1109/ICOEI53556.2022.9776967
Dash S., Das R.K., Guha S., Bhagat S.N., Behera G.K. (2021) An Interactive Machine Learning Approach for Brain Tumor MRI Segmentation. In: Das S., Mohanty M.N. (eds) Advances in Intelligent Computing and Communication. Lecture Notes in Networks and Systems, vol 202. Springer, Singapore. https://doi.org/10.1007/978-981-16-0695-3_38 DOI: https://doi.org/10.1007/978-981-16-0695-3_38
Dutta, Stobak, Sachikanta Dash, and Neelamadhab Padhy. "Analysis of Human Emotion- Based Data Using MIoT Technique." Medical Internet of Things: Techniques, Practices and Applications (2021): (pp. 199-203). Chapman and Hall/CRC. DOI: https://doi.org/10.1201/9780429318078-13
Pradosh Kumar Gantayat, Sachikanta Dash, 2020 “Liver Disease Prediction Using Machine Learning Algorithm” , Data Engineering and Intelligent Computing, Proceedings of ICICC 2020, https://link.springer.com/chapter, doi:10.1007/978-981-16-0171-2 DOI: https://doi.org/10.1007/978-981-16-0171-2
Dash S., Gantayat P.K., Das R.K. (2021) Blockchain Technology in Healthcare: Opportunities and Challenges. In: Panda S.K., Jena A.K., Swain S.K., Satapathy S.C. (eds) Blockchain Technology: Applications and Challenges. Intelligent Systems Reference Library, vol 203. Springer, Cham. https://doi.org/10.1007/978-3-030-69395-4_6 DOI: https://doi.org/10.1007/978-3-030-69395-4_6
Shen, Y., & Ke, J. (2021). Sampling based tumor recognition in whole-slide histology image with deep learning approaches. IEEE/ACM Transactions on Computational Biology and Bioinformatics. DOI: https://doi.org/10.1109/TCBB.2021.3062230
Sasmal, Pradipta, et al. "Extraction of Key-Frames From Endoscopic Videos by Using Depth Information." IEEE Access 9 (2021): 153004-153011. DOI: https://doi.org/10.1109/ACCESS.2021.3126835
Jia, Xiao, et al. "Automatic polyp recognition in colonoscopy images using deep learning and two-stage pyramidal feature prediction." IEEE Transactions on Automation Science and Engineering 17.3 (2020): 1570-1584.
Yang, Xiaoyong, et al. "Colon polyp detection and segmentation based on improved MRCNN." IEEE Transactions on Instrumentation and Measurement 70 (2020): 1-10. DOI: https://doi.org/10.1109/TIM.2020.3038011
Tan, Jiaxing, et al. "3D-GLCM CNN: A 3-dimensional gray-level Co-occurrence matrix- based CNN model for polyp classification via CT colonography." IEEE transactions on medical imaging 39.6 (2019): 2013-2024. DOI: https://doi.org/10.1109/TMI.2019.2963177
Adweb, Khaled Mabrouk Amer, Nadire Cavus, and Boran Sekeroglu. "Cervical cancer diagnosis using very deep networks over different activation functions." IEEE Access 9 (2021): 46612-46625. DOI: https://doi.org/10.1109/ACCESS.2021.3067195
Kiehl, Lennard, et al. "Deep learning can predict lymph node status directly from histology in colorectal cancer." European Journal of Cancer 157 (2021): 464-473. DOI: https://doi.org/10.1016/j.ejca.2021.08.039
Mulenga, Mwenge, et al. "Stacking and chaining of normalization methods in deep learning-based classification of colorectal cancer using gut microbiome data." IEEE Access 9 (2021): 97296-97319. DOI: https://doi.org/10.1109/ACCESS.2021.3094529
Lee, J., Oh, J. E., Kim, M. J., Hur, B. Y., & Sohn, D. K. (2019). Reducing the model variance of a rectal cancer segmentation network. IEEE Access, 7, 182725-182733. DOI: https://doi.org/10.1109/ACCESS.2019.2960371
Paikaray, B. K., Swain, D., & Chakravarty, S. (2021). Reversible selective embedding for DICOM image security and integrity using visual cryptography. International Journal of Electronic Security and Digital Forensics, 13(5), 498-514. DOI: https://doi.org/10.1504/IJESDF.2021.117306
A. B. Dash, S. Dash, S. Padhy, B. Mishra and A. N. Singh, "Analysis of Brain Function Effecting Form the Tumor Disease Using the Image Segmentation Technique," 2022 Second International Conference on Computer Science, Engineering and Applications (ICCSEA), 2022, pp. 1-6, doi: 10.1109/ICCSEA54677.2022.9936523. DOI: https://doi.org/10.1109/ICCSEA54677.2022.9936523
Dash, A. B., Mishra, B., & Singh, A. N. "Identification of Premature Diagnosis for Detection of Brain Tumor Using Blockchain Strategy," in Springer,In Next Generation of Internet of Things, Singapore., 2021, pp. 31-38. DOI: https://doi.org/10.1007/978-981-16-0666-3_4
Paikaray, B. K., Swain, D., & Chakravarty, S. (2023). An improved region-based embedding technique for data hiding and image recovery using multiple ROI and RONI. International Journal of Electronic Security and Digital Forensics, 15(2), 101-113. DOI: https://doi.org/10.1504/IJESDF.2023.129269
B. Mishra et, al. (In Press) Identification and Detection of Brain Tumour Using Deep Learning-based Classification MRI Applied Using Neural Network and Machine Learning Algorithm, International Journal of Reasoning-based Intelligent Systems.
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