Machine learning with different digital images classification in laparoscopic surgery
DOI:
https://doi.org/10.12775/JEHS.2022.12.03.025Keywords
machine learning, images analysis, HAAR features cascade, AdaBoost classifier, laparoscopic surgeryAbstract
The evaluation of the effectiveness of the automatic computer diagnostic (ACD) systems developed based on two classifiers – HAAR features cascade and AdaBoost for the laparoscopic diagnostics of appendicitis and ovarian cysts in women with chronic pelvic pain is presented. The training of HAAR features cascade, and AdaBoost classifiers were performed with images/ frames, which have been extracted from video gained in laparoscopic diagnostics. Both gamma-corrected RGB and RGB converted into HSV frames were used for training. Descriptors were extracted from images with the method of Local Binary Pattern (LBP), which includes both data on color characteristics («modified color LBP» - MCLBP) and textural characteristics, which have been used later on for AdaBoost classifier training. Classification of test video images revealed that the highest recall for appendicitis diagnostics was achieved after training of AdaBoost with MCLBP descriptors extracted from RGB images – 0.708, and in the case of ovarian cysts diagnostics – for MCLBP gained from RGB images – 0.886.
Developed AdaBoost-based ACD system achieved a 73.6% correct classification rate (accuracy) for appendicitis and 85.4% for ovarian cysts. The accuracy of the HAAR features classifier was highest in the case of ovarian cysts identification and achieved 0.653 (RGB) – 0.708 (HSV) values. It was concluded that the HAAR feature-based cascade classifier turned to be less effective when compared with the AdaBoost classifier trained with MCLBP descriptors. Ovarian cysts were better diagnosed when compared with appendicitis with the developed ACD.
References
Albisser, Z.: Computer-aided screening of capsule endoscopy videos. Master's Thesis, University of Oslo. (2015) .Available at: https://www.duo.uio.no/handle/10852/47642
Bay, H, Tuytelaars, T, Van Gool, L.: Surf: Speeded up robust features. Computer vision–ECCV. 3951, 404-417 (2006). doi: 10.1007/11744023_32
Bayazitov, D.N., Kresyun, N.V., Buzіnovsky, А.B., Bayazitov, N.R., Lyashenko, A.V., Godlevsky, L.S., Prybolovets, T.V., Bidnyuk, K.A.: The effectiveness of automatic laparoscopic diagnostics of liver pathology using different methods of digital images classification. Pathologiya [Ukraine]. 14, 182–187 (2017). doi: 10.14739/2310-1237. 2017.2.109219
Fatiev, D.: Faculty of Science and Technology Department of Computer Science Object tracking for improved telementoring and telestration. Master's Thesis in Telemedicine and E-health, The Arctic University of Norway. (2015). Available at: https://hdl.handle.net/10037/7764
Godlevsky L. S., Kresyun N. V., Martsenyuk V. P., Shakun K. S., Tatarchuk T. V., Prybolovets K. O., Kalinichenko L. F., Karpinski M., Gancarczyk T. Information System for Early Diabetic Retinopathy Diagnostics based on Multiscale Texture Gradient Method. World Academy of Science, Engineering and Technology International Journal of Medical and Health Sciences Vol:14, No:8, 2020, waset.org/Publication/10011395. Available at: https://repo.odmu.edu.ua:443/xmlui/handle/123456789/8296
Godlevsky L.S., Shakun K.S., Martsenyuk V.P., Tatarchuk T.V., Stoeva T.V., Godlevska T.L., Shakun I.K.,Klos- Witkowska A. (2019). Dynamic changes of the color intensity of collected urine as a basis for a distant uroflowmetry. In: Proc. The 10 th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications. 18-21 September, Metz, France. Pp. 308-312. DOI: 10.1109/IDAACS.2019.8924436
Guo, Z., Zhang, L., Zhang, D.: A completed modeling of local binary pattern operator for texture classification. In: IEEE Transactions on Image Processing, pp. 1657-1663, Publisher: Institute of Electrical and Electronics Engineers (2010). doi: 10.1109/TIP.2010.2044957
Hashimoto, D.A., Rosman G., Rus, D., Meireles O.R.: Artificial Intelligence in Surgery: Promises and Perils. Ann.Surg. 268, 70-76. (2018). doi: 10.1097/SLA.0000000000002693
Lahane, A., Yesha, Y., Grasso, M., Joshi, A., Park, A., Lo, J.: Detection of unsafe action from laparoscopic cholecystectomy video. In: Proc. 2nd ACM SIGHIT International Health Informatics Symposium, pp. 315-322. Association for Computing Machinery Press, New York (2012). https://doi.org/10.1145/2110363.2110400
Ledoux, A., Losson, O., Macaire, L.: Color local binary patterns: compact descriptors for texture classification. Journal of Electronic Imaging, 25(6), 061404. (2016). doi: 10.1117/1.JEI.25.6.061404
Lyashenko, A.V., Bayazitov, N.R., Godlevsky, L.S., Bayazitov, N.R., Buzinovsky, A.B.: Informational -technical system for the automatized laparoscopic diagnostics. Radio Electronics, Computer Science, Control [Ukraine]. 4, 90-96. (2016). Available at: http://repo.odmu.edu.ua:80/xmlui/handle/123456789/5532
Madad Zadeh, S., Francois, T., Calvet, L. Chauvet, P., Canis, M., Bartoli, A., Bourdel, N.: SurgAI: deep learning for computerized laparoscopic image understanding in gynaecology. Surg. Endosc. (2020). https://doi.org/10.1007/s00464-019-07330-8
Neofytou, M.S., Tanos, V., Constantinou, I., Kyriacou, E.C., Pattichis M.S., Pattichis, C.S.: Computer-aided diagnosis in hysteroscopic imaging. IEEE J. Biomed. Health. Inform. 19(3), 1129-1136. (2015). doi: 10.1109/JBHI.2014.2332760
Petscharnig, S., Schöffmann, K.: Learning laparoscopic video shot classification for gynecological surgery. Multimed. Tools Appl. 77, 8061–8079. (2018). doi: 10.1007/s11042-017-4699-5
Shu, Y., Bilodeau, G.A., Cheriet F.: Segmentation of laparoscopic images: Integrating graph-based segmentation and multistage region merging. In: Proc. 2nd Canadian Conference on Computer and Robot Vision (CRV’05) (2005). doi: 10.1109/ CRV. 2005.74
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In:Proc IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 511-518. (2001). doi: 10.1109/CVPR.2001.990517
Zhou, M, Bao, G, Geng, Y, Alkandari, B, Li, X.: Polyp detection and radius measurement in small intestine using video capsule endoscopy. In: Biomedical Engineering and Informatics (BMEI), 2014 7th International Conference on, IEEE, pp. 237-241. (2014). doi: 10.1109/BMEI.2014.7002777
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2022 M. Bayazitov, A. Liashenko, D. Bayazitov, T. Stoeva, T. Godlevska
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
The periodical offers access to content in the Open Access system under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0
Stats
Number of views and downloads: 272
Number of citations: 0
