HiFormer: a fast and accurate deep learning model for segmentation and detection of glaucoma

Mamyrbayev O. Zhanegizov A. Momynzhanova K. Keylan A.
January 2026 Springer

Journal of Supercomputing
2026 #82 Issue 1

Glaucoma is a leading cause of irreversible blindness, and early detection is critical for preventing vision loss. This paper presents HiFormer, a hybrid deep learning model that combines convolutional neural networks (CNNs) with a hierarchical attention-based transformer architecture to improve the segmentation and classification of fundus images. Unlike traditional CNN-based methods, HiFormer leverages hierarchical attention to better capture multi-scale features and subtle pathological changes associated with glaucoma. The model is trained and evaluated on both open-source datasets and clinical data from the Scientific Research Institute of Eye Diseases in Kazakhstan. Experimental results demonstrate that HiFormer outperforms standard CNN architectures, achieving a 2.91% increase in Dice coefficient and a 16.99 unit decrease in average Hausdorff Distance (HD) compared to TransUnet, and a 1.26% increase in Dice coefficient and 6.85 unit decrease in average HD compared to Swin-Unet. These metrics indicate enhanced accuracy, sensitivity, and computational efficiency, making it well-suited for real-time clinical applications. This work contributes to developing faster and more reliable diagnostic tools for glaucoma detection using retinal imaging.

Computational efficiency , Glaucoma detection , Hybrid neural networks

Text of the article Перейти на текст статьи

Laboratory of Computer Engineering of Intelligent Systems, Institute of Information and Computational Technologies, 28 Shevchenko, Almaty, 050010, Kazakhstan
Faculty of Mechanics and Mathematics, L.N. Gumilyov Eurasian National University, 2 Satbaev, Astana, 10000, Kazakhstan

Laboratory of Computer Engineering of Intelligent Systems
Faculty of Mechanics and Mathematics

10 лет помогаем публиковать статьи Международный издатель

Книга Публикация научной статьи Волощук 2026 Book Publication of a scientific article 2026