Systematic Study on Recent Automatic Microaneurysm Detection for Diabetic Retinopathy
DOI:
https://doi.org/10.29070/67ytps79Keywords:
Automatic Microaneurysyms, Diabetic Retinopathy, medical, deep learningAbstract
One of the most common causes of vision loss in people with diabetes is diabetic retinopathy(DR), a chronic illness characterised by damage to the retina as a result of tiny vessel damage broughton by the disease. ―The scientific community has spent the better part of the last several years focusingon the issue of microaneurysyms (MA) segmentation since it is essential for the early identification ofDR. In this study, the diagnostic utility of automated MA detection and segmentation for early DRdiagnosis is investigated using a comprehensive literature analysis. In particular, we analyse thebenefits and drawbacks of currently available early DR diagnostic approaches. Our research is confinedto colour fundus photography since it is the most often used method for early diagnosis. Thedevelopment of completely automated, user-friendly early DR diagnosis and grading systems has a greatdeal of room to grow, despite the fact that much progress has been made in these three categories ofearly DR diagnosis.‖Downloads
References
Micro aneurysm detection by multiple feature subset extraction and selection based on SVM-weights and Genetic Algorithm-Neural Network
Automated Microaneurysyms Detection in Retinal Images Using Radon Transform and Supervised Learning: Application to Mass Screening of Diabetic Retinopathy.
Automated Microaneurysyms Detection from Retinal Fundus Images using Pixel Intensity Rank Transform
Secondary Observer System for Detection of Microaneurysyms in Fundus Images Using Texture Descriptors
B. Dupas, T. Walter, A. Erginay, R. Ordonez, N. Deb-Joardar, P. Gain, J.-C. Klein, and P. Massin, “Evaluation of automated fundus photograph analysis algorithms for detecting microaneurysms, haemorrhages and exudates, and of a computer-assisted diagnostic system for grading diabetic retinopathy,” Diabetes & metabolism, vol. 36, no. 3, pp. 213–220, 2010.
B. Zhang, K. Karray, L. Zhang, and J. You, “Microaneurysyms (ma) detection via sparse representation classifier with ma and non-madictionary learning,” in Pattern Recognition (ICPR), 2010 20th International Conference on. IEEE, 2010, pp. 277–280.
Manjaramkar and M. Kokare, “A rule based expert system for microaneurysm detection in digital fundus images,” in Computational Techniques in Information and Communication Technologies (ICC-TICT), 2016 International Conference on. IEEE, 2016, pp. 137–140.
Zhang, F. Karray, Q. Li, and L. Zhang, “Sparse representation classifier for microaneurysm detection and retinal blood vessel extraction,” Information Sciences, vol. 200, pp. 78–90, 2012.
M. Niemeijer, B. Van Ginneken, M. J. Cree, A. Mizutani, G. Quel- lec, C. Sánchez, B. Zhang, R. Hornero, M. Lamard, C. Muramatsu et al., “Retinopathy online challenge: automatic detection of microa-neurysms in digital color fundus photographs,” Medical Imaging, IEEE Transactions on, vol. 29, no. 1, pp. 185–195, 2010.
R. Pourreza, H. Pourreza, and T. Banaee, “Segmentation of blood vessels in fundus color images by radon transform and morphological reconstruction,” in Advanced Computational Intelligence (IWACI), 2010 Third International Workshop on. IEEE, 2010, pp. 522–526.
Ganesh, S & Basha, AM 2015, ‘Automated Detection of Diabetic Retinopathy using retinal optical images’, International Journal of Science, Technology & Management, vol. 4, no. 2,pp.136-144.
Jahiruzzaman, MD&Aowlad Hossain ABM,2015,’Detection and classification of diabetic Retinopathyusing k-means clustering and Fuzzy Logic’, International Conference on Computer and information Technology.
Falguni Thakkar & Rajvi Parikh 2016, ‘A Survey on Automatic Detection of Diabetic Retinopathy Exudates from Retinal Fundus Images’, International Journal of Advanced Research in Computer and Communication Engineering,vol. 5, no. 5,pp.775-778.
Ragav Venkatesan,Paraa S Chandakkar &Baoxin Li2017,’MIRank-KNN: multiple-instance retrieval of clinically relevant diabetic retinopathy images’International Journal of Medical Imaging ,vol.4,no.3.
Prakash, NB, Hemalakshmi, GR & Stella Inba Mary, M 2016, ‘Automated grading of Diabetic Retinopathy stages in fundus images using SVM classifer’, Journal of Chemical and Pharmaceutical Research, vol.8, no.1, pp.637-541.
Shrutika A Patil & Manjiri Gogate 2017, ‘Automatic Screening and Classification using Machine Analysis Technique’, International Conference On Emanations in Modern Technology and Engineering (ICEMTE-2017) ISSN: 2321-8169, vol. 5, no. 3.
Amreen Taj, C, Annapoorna, M, Deepika, KH, Keerthi Kumari, BA & Kanimozhi, S 2017, ‘Detection of Exudates in Retinal Images using Support Vector Machine’, International Research Journal of Engineering and Technology (IRJET), vol. 4, no. 5,pp.1847-1852.
Malathi, K & Nedunchelian, R 2017, ‘A recursive support vector machine (RSVM) algorithm to detect and classify Diabetic Retinopathy in fundus retina images”. Biomedical Research, pp. 1-8.
Sagar Honnungar, Sanyam Mehra & Samuel Joseph 2016, ‘Diabetic Retinopathy Identification and Severity Classification’, CS229, FALL, pp. 1-5.
Monzurul Islam, Anh Dinh, V & Khan Wahid, A 2017, ‘Automated Diabetic Retinopathy Detection Using Bag of Words Approach’, J. Biomedical Science and Engineering, vol.10, pp.86-96.
Jadhav, M.L., Shaikh, M.Z., Sardar, V.M. (2021). Automated Microaneurysyms Detection in Fundus Images for Early Diagnosis of Diabetic Retinopathy. In: Bhateja, V., Satapathy, S.C., Travieso-González, C.M., Aradhya, V.N.M. (eds) Data Engineering and Intelligent Computing. Advances in Intelligent Systems and Computing, vol 1407. Springer, Singapore.
Zhu, CZ., Hu, R., Zou, BJ. et al. Automatic Diabetic Retinopathy Screening via Cascaded Framework Based on Image- and Lesion-Level Features Fusion. J. Comput. Sci. Technol. 34, 1307–1318 (2019).
Veena Mayya, Sowmya Kamath S․, Uma Kulkarni, Automated microaneurysyms detection for early diagnosis of diabetic retinopathy: A Comprehensive review, Computer Methods and Programs in Biomedicine Update, Volume 1, 2021, 100013, ISSN 2666-9900,
Mateen M, Malik TS, Hayat S, Hameed M, Sun S, Wen J. Deep Learning Approach for Automatic Microaneurysyms Detection. Sensors (Basel). 2022 Jan 11;22(2):542. doi: 10.3390/s22020542. PMID: 35062506; PMCID: PMC8781897.
Long, S., Chen, J., Hu, A. et al. Microaneurysyms detection in colour fundus images using machine learning based on directional local contrast. BioMed Eng OnLine 19, 21 (2020).
Wankhede P. R, Khanchandani K. B. Automated Microaneurysyms Detection from Retinal Fundus Images using Pixel Intensity Rank Transform. Biomed Pharmacol J 2020;13(1).
Jen Hong Tan, Hamido Fujita, Sobha Sivaprasad, Sulatha V. Bhandary, A. Krishna Rao, Kuang Chua Chua, U. Rajendra Acharya, Automated segmentation of exudates, haemorrhages, microaneurysyms using single convolutional neural network, Information Sciences, Volume 420, 2017, Pages 66-76, ISSN 0020-0255,
Yinhan Liao, Haiying Xia, Shuxiang Song, Haisheng Li, Microaneurysm detection in fundus images based on a novel end-to-end convolutional neural network, Biocybernetics and Biomedical Engineering, Volume 41, Issue 2, 2021, Pages 589-604, ISSN 0208-5216,
Y. Hatanaka, K. Ogohara, W. Sunayama, M. Miyashita, C. Muramatsu and H. Fujita, "Automatic microaneurysyms detection on retinal images using deep convolution neural network," 2018 International Workshop on Advanced Image Technology (IWAIT), 2018, pp. 1-2,
S. B. Patil and B. P. Patil, "Automatic Detection of Microaneurysyms in Retinal Fundus Images using Modified High Boost Filtering, Line Detectors and OC-SVM," 2020 International Conference on Industry 4.0 Technology (I4Tech), 2020, pp. 148-153
M. Tavakoli, A. Mehdizadeh, A. Aghayan, R. P. Shahri, T. Ellis and J. Dehmeshki, "Automated Microaneurysyms Detection in Retinal Images Using Radon Transform and Supervised Learning: Application to Mass Screening of Diabetic Retinopathy," in IEEE Access, vol. 9, pp. 67302-67314, 2021,
Mateen, M.; Malik, T.S.; Hayat, S.; Hameed, M.; Sun, S.; Wen, J. Deep Learning Approach for Automatic Microaneurysyms Detection. Sensors 2022, 22, 542.
Islam, Md Mohaimenul, et al. "Deep learning algorithms for detection of diabetic retinopathy in retinal fundus photographs: A systematic review and meta-analysis." Computer Methods and Programs in Biomedicine 191 (2020): 105320.
M.H. Sarhan, M.A. Nasseri, D. Zapp, M. Maier, C.P. Lohmann, N. Navab, A. Eslami Machine learning techniques for ophthalmic data processing: a review IEEE J. Biomed. Health Inform., 24 (12) (2020), pp. 3338-3350,
M.Y.T. Yip, G. Lim, Z.W. Lim, et al. Technical and imaging factors influencing performance of deep learning systems for diabetic retinopathy npj Digit. Med., 3 (1) (2020)
D.V. Gunasekeran, D.S.W. Ting, G.S.W. Tan, T.Y. Wong Artificial intelligence for diabetic retinopathy screening, prediction and management Curr. Opin. Ophthalmol., 31 (5) (2020), pp. 357-365
K. Mittal, V.M.A. Rajam Computerized retinal image analysis - a survey Multimed. Tools Appl., 79 (31–32) (2020), pp. 22389-22421
S. Stolte, R. Fang A survey on medical image analysis in diabetic retinopathy Med. Image Anal., 64 (2020)
U. Ishtiaq, S. Abdul Kareem, E.R.M.F. Abdullah, G. Mujtaba, R. Jahangir, H.Y. Ghafoor Diabetic retinopathy detection through artificial intelligent techniques: a review and open issues Multimed. Tools Appl., 79 (21–22) (2020), pp. 15209-15252.
Mahajan, H.B., Badarla, A. & Junnarkar, A.A. CL-IoT: cross-layer Internet of Things protocol for intelligent manufacturing of smart farming. J Ambient Intell Human Comput 12, 7777–7791 (2021). https://doi.org/10.1007/s12652-020-02502-0
Mahajan, H.B., & Badarla, A. (2018). Application of Internet of Things for Smart Precision Farming: Solutions and Challenges. International Journal of Advanced Science and Technology, Vol. Dec. 2018, PP. 37-45.
Mahajan, H.B., & Badarla, A. (2019). Experimental Analysis of Recent Clustering Algorithms for Wireless Sensor Network: Application of IoT based Smart Precision Farming. Jour of Adv Research in Dynamical & Control Systems, Vol. 11, No. 9. 10.5373/JARDCS/V11I9/20193162.
Mahajan, H.B., & Badarla, A. (2020). Detecting HTTP Vulnerabilities in IoT-based Precision Farming Connected with Cloud Environment using Artificial Intelligence. International Journal of Advanced Science and Technology, Vol. 29, No. 3, pp. 214 - 226.
Mikhail, A., Kamil, I. A., & Mahajan, H. (2017). Increasing SCADA System Availability by Fault Tolerance Techniques. 2017 International Conference on Computing, Communication, Control and Automation (ICCUBEA). doi:10.1109/iccubea.2017.8463911
Mikhail, A., Kareem, H. H., & Mahajan, H. (2017). Fault Tolerance to Balance for Messaging Layers in Communication Society. 2017 International Conference on Computing, Communication, Control and Automation (ICCUBEA). doi:10.1109/iccubea.2017.8463871
Alhayani, B., Abbas, S.T., Mohammed, H.J., & Mahajan, H. B. Intelligent Secured Two-Way Image Transmission Using Corvus Corone Module over WSN. Wireless Pers Commun (2021). https://doi.org/10.1007/s11277-021-08484-2.
Mahajan, H.B., Badarla, A. Cross-Layer Protocol for WSN-Assisted IoT Smart Farming Applications Using Nature Inspired Algorithm. Wireless Pers Commun 121, 3125–3149 (2021). https://doi.org/10.1007/s11277-021-08866-6
Uke, N., Pise, P., Mahajan, H.B., et.al. (2021). Healthcare 4.0 Enabled Lightweight Security Provisions for Medical Data Processing. Turkish Journal of Computer and Mathematics (2021), Vol. 12, No. 11. https://doi.org/10.17762/turcomat.v12i11.5858.
