Abstract
Manual interpretation and retrieval of colorectal polyps is a time-consuming and laborious task even for specialized medical experts. An automated system can help in information retrieval and timely treatment of polyps. This work comprises of a colonoscopy polyp image retrieval and detection pipeline through the proposed Convolutional Neural Network (CNN) architecture. A binary classification of polyps versus non-polyps has been carried out to retrieve information about polyps in the colonoscopic frames. To check the efficacy of the architecture, test set evaluation, feature mapping, and per epoch analysis of achieved loss and accuracy values have been done. An improved Jaccard index of 83.18% and specificity up to 94.50% have been reported for 33,000 polyp and non-polyp frames generated using publicly available colonoscopic databases. Results infer a maximum of 206 correctly detected polyps out of 215 polyp image frames. The developed architecture has also been compared with state-of-the-art work in this field.
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References
Ali S, Zhou F, Daul C, Braden B, Bailey A, Realdon S, East J, Wagnieres G, Loschenov V, Grisan E et al (2019) Endoscopy artifact detection (ead 2019) challenge dataset. arXiv:1905.03209
de Almeida Thomaz V, Sierra-Franco CA, Raposo AB (2021) Training data enhancements for improving colonic polyp detection using deep convolutional neural networks. Artif Intell Med 111:101988
Azer SA (2019) Challenges facing the detection of colonic polyps: what can deep learning do? Medicina 55(8):473
Bernal J, Tajkbaksh N, Sanchez FJ, Matuszewski BJ, Chen H, Yu L, Angermann Q, Romain O, Rustad B, Balasingham I et al (2017) Comparative validation of polyp detection methods in video colonoscopy: results from the miccai 2015 endoscopic vision challenge. IEEE Trans Med Imaging 36(6):1231–1249
Cao C, Wang R, Yu Y, Zhang H, Yu Y, Sun C (2021) Gastric polyp detection in gastroscopic images using deep neural network. Plos One 16(4):e0250632
Dureja A, Pahwa P (2019) Analysis of non-linear activation functions for classification tasks using convolutional neural networks. Recent Pat Comput Sci 12(3):156–161
Goel N, Kaur S, Gunjan D, Mahapatra S (2022) Dilated cnn for abnormality detection in wireless capsule endoscopy images. Soft Comput 26(3):1231–1247
Goel N, Kaur S, Gunjan D, Mahapatra S (2022) Investigating the significance of color space for abnormality detection in wireless capsule endoscopy images. Biomed Signal Process Control 75:103624
Haggar FA, Boushey RP (2009) Colorectal cancer epidemiology: incidence, mortality, survival, and risk factors. Clin Colon Rectal Surg 22(04):191–197
Haj-Manouchehri A, Mohammadi HM (2020) Polyp detection using cnns in colonoscopy video. IET Comput Vis 14(5):241–247
Handa P, Goel N, Indu S (2022) Datasets of wireless capsule endoscopy for AI-enabled techniques. In: Raman B, Murala S, Chowdhury A, Dhall A, Goyal P (eds) Computer vision and image processing. Springer International Publishing, Cham, pp 439–446
Kaur S, Goel N (2020) A dilated convolutional approach for inflammatory lesion detection using multi-scale input feature fusion (workshop paper). In: 2020 IEEE sixth international conference on multimedia big data (BigMM), pp 386–393. https://doi.org/10.1109/BigMM50055.2020.00066
Krenzer A, Banck M, Makowski K, Hekalo A, Fitting D, Troya J, Sudarevic B, Zoller WG, Hann A, Puppe F (2023) A real-time polyp-detection system with clinical application in colonoscopy using deep convolutional neural networks. J Imaging 9(2):26
Li T, Brown JRG, Tsourides K, Mahmud N, Cohen JM, Berzin TM (2020) Training a computer-aided polyp detection system to detect sessile serrated adenomas using public domain colonoscopy videos. Endosc Int Open 8(10):E1448–E1454
Nogueira-Rodríguez A, Domínguez-Carbajales R, López-Fernández H, Iglesias Á, Cubiella J, Fdez-Riverola F, Reboiro-Jato M, Glez-Peña D (2021) Deep neural networks approaches for detecting and classifying colorectal polyps. Neurocomputing 423:721–734
Pogorelov K, Riegler M, Eskeland SL, de Lange T, Johansen D, Griwodz C, Schmidt PT, Halvorsen P (2017) Efficient disease detection in gastrointestinal videos-global features versus neural networks. Multimed Tools Appl 76(21):22493–22525
Qadir HA, Balasingham I, Solhusvik J, Bergsland J, Aabakken L, Shin Y (2019) Improving automatic polyp detection using cnn by exploiting temporal dependency in colonoscopy video. IEEE J Biomed Health Inform 24(1):180–193
Rahim T, Hassan SA, Shin SY (2021) A deep convolutional neural network for the detection of polyps in colonoscopy images. Biomed Signal Process Control 68:102654
Sánchez-Montes C, Bernal J, García-Rodríguez A, Córdova H, Fernández-Esparrach G (2020) Review of computational methods for the detection and classification of polyps in colonoscopy imaging. Gastroenterología y Hepatología (English Edition) 43(4):222–232
Sánchez-Peralta LF, Bote-Curiel L, Picón A, Sánchez-Margallo FM, Pagador JB (2020) Deep learning to find colorectal polyps in colonoscopy: a systematic literature review. Artif Intell Med 101923
Tashk A, Herp J, Nadimi E (2019) Fully automatic polyp detection based on a novel u-net architecture and morphological post-process. In: 2019 international conference on control, artificial intelligence, robotics and optimization (ICCAIRO). IEEE, pp 37–41
Tavanapong W, Pratt J, Oh J, Khaleel M, Wong JS, de Groen PC (2023) Development and deployment of computer-aided real-time feedback for improving quality of colonoscopy in a multi-center clinical trial. Biomed Signal Process Control 83:104609
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Handa, P., Sachdeva, R.A., Goel, N. (2023). CNN Architecture-Based Image Retrieval of Colonoscopy Polyp Frames. In: Yadav, A., Gupta, G., Rana, P., Kim, J.H. (eds) Proceedings on International Conference on Data Analytics and Computing. ICDAC 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 175. Springer, Singapore. https://doi.org/10.1007/978-981-99-3432-4_2
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