Medical Image Processing and Deep Learning Models and Algorithms. (For Eye Diseases)

Kuljanova Shukurjon Zaribovna; Khujayev Otabek

Medical Image Processing and Deep Learning Models and Algorithms. (For Eye Diseases)

Kuljanova Shukurjon Zaribovna

Urgench branch of Tashkent information technology university 2nd year magistry student

Khujayev Otabek

Scientific supervisor Kadamboyevich Department of Computer Engineering Urganch Branch of Tashkent University of Information Technologies named after Muhammad al-Khwarizmi Urgench Uzbekiston

##semicolon## Medical image processing, deep learning, eye diseases, ophthalmology, artificial intelligence, neural networks, image analysis, diagnostic tools.

सार

Medical image processing and deep learning models are revolutionizing the field of ophthalmology. These techniques enhance the accuracy and efficiency of diagnosing eye diseases by analyzing vast amounts of imaging data. This paper reviews the latest advancements in medical image processing and deep learning algorithms specifically applied to eye diseases, highlighting their applications, benefits, and challenges.

##submission.citations##

Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., ... & Webster, D. R. (2016). Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA, 316(22), 2402-2410.

Ting, D. S. W., Pasquale, L. R., Peng, L., Campbell, J. P., Lee, A. Y., Raman, R., ... & Wong, T. Y. (2019). Artificial intelligence and deep learning in ophthalmology. British Journal of Ophthalmology, 103(2), 167-175.

De Fauw, J., Ledsam, J. R., Romera-Paredes, B., Nikolov, S., Tomasev, N., Blackwell, S., ... & Hassabis, D. (2018). Clinically applicable deep learning for diagnosis and referral in retinal disease. Nature medicine, 24(9), 1342-1350.

Kermany, D. S., Goldbaum, M., Cai, W., Valentim, C. C. S., Liang, H., Baxter, S. L., ... & Dong, J. (2018). Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell, 172(5), 1122-1131.

Lee, C. S., Tyring, A. J., Deruyter, N. P., Wu, Y., & Rokem, A. (2017). Deep-learning based, automated segmentation of macular edema in optical coherence tomography. Biomedical optics express, 8(7), 3440-3448.

Abramoff, M. D., Lavin, P. T., Birch, M., Shah, N., & Folk, J. C. (2018). Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices. NPJ Digital Medicine, 1(1), 39.

Leibig, C., Allken, V., Berens, P., Wahl, S., & Schubert, B. (2016). Leveraging uncertainty information from deep neural networks for disease detection. Scientific Reports, 7, 17816.

Bellemo, V., Lim, Z. W., Lim, G., Nguyen, Q. D., Xie, Y., Yip, M. Y. T., ... & Ting, D. S. W. (2019). Artificial intelligence using deep learning to screen for referable and vision-threatening diabetic retinopathy in Africa: a clinical validation study. The Lancet Digital Health, 1(1), e35-e44.

Gargeya, R., & Leng, T. (2017). Automated identification of diabetic retinopathy using deep learning. Ophthalmology, 124(7), 962-969.

Yan, Q., Weeks, D. E., Xin, H., Swaroop, A., Chew, E. Y., Huang, H., & Chakravarti, A. (2018). Deep-learning based prediction of late age-related macular degeneration progression. Nature Machine Intelligence, 2(3), 141-150.

Venhuizen, F. G., van Ginneken, B., van Aken, B. B., Maaijwee, K. J. M., Droog, E. J., Verbraak, F. D., ... & Sánchez, C. I. (2017). Automated age-related macular degeneration classification in OCT using unsupervised feature learning. Investigative ophthalmology & visual science, 58(6), 2318-2328.

Schlegl, T., Waldstein, S. M., Bogunović, H., Sadeghipour, A., Langs, G., & Schmidt-Erfurth, U. (2018). Fully automated detection and quantification of macular fluid in OCT using deep learning. Ophthalmology, 125(4), 549-558.

Li, Z., He, Y., Keel, S., Meng, W., Chang, R. T., & He, M. (2018). Efficacy of a deep learning system for detecting glaucomatous optic neuropathy based on color fundus photographs. Ophthalmology, 125(8), 1199-1206.

Asaoka, R., Murata, H., Iwase, A., Yamashita, T., & Araie, M. (2019). Detecting preperimetric glaucoma with standard automated perimetry using deep learning technology. Ophthalmology, 126(5), 1024-1032.

Raju, M., Li, C., & Lu, W. (2019). Automated diabetic retinopathy detection using images from different fundus cameras through deep learning. IEEE Access, 7, 122869-122878.

Sandhu, H. S., Li, Y., Chen, M., Tang, J., & Hwang, D. K. (2018). Automated detection of diabetic retinopathy using fundus image analysis. Expert Systems with Applications, 103, 232-241.

Ramachandran, N., Hong, S. C., Sime, M. J., & Wilson, G. A. (2018). The evaluation of artificial intelligence-based grading of diabetic retinopathy in primary care. NPJ Digital Medicine, 1(1), 39.