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. 2024 Jul 17;10(14):e34726.
doi: 10.1016/j.heliyon.2024.e34726. eCollection 2024 Jul 30.

NCME-Net: Nuclear cataract mask encoder network for intelligent grading using self-supervised learning from anterior segment photographs

Jiani Zhao  1 , Cheng Wan  1 , Jiajun Li  2 , Zhe Zhang  3 , Weihua Yang  3 , Keran Li  2
Affiliations

NCME-Net: Nuclear cataract mask encoder network for intelligent grading using self-supervised learning from anterior segment photographs

Jiani Zhao et al. Heliyon. .

Abstract

Cataracts are a leading cause of blindness worldwide, making accurate diagnosis and effective surgical planning critical. However, grading the severity of the lens nucleus is challenging because deep learning (DL) models pretrained using ImageNet perform poorly when applied directly to medical data due to the limited availability of labeled medical images and high interclass similarity. Self-supervised pretraining offers a solution by circumventing the need for cost-intensive data annotations and bridging domain disparities. In this study, to address the challenges of intelligent grading, we proposed a hybrid model called nuclear cataract mask encoder network (NCME-Net), which utilizes self-supervised pretraining for the four-class analysis of nuclear cataract severity. A total of 792 images of nuclear cataracts were categorized into the training set (533 images), the validation set (139 images), and the test set (100 images). NCME-Net achieved a diagnostic accuracy of 91.0 % on the test set, a 5.0 % improvement over the best-performing DL model (ResNet50). Experimental results demonstrate NCME-Net's ability to distinguish between cataract severities, particularly in scenarios with limited samples, making it a valuable tool for intelligently diagnosing cataracts. In addition, the effect of different self-supervised tasks on the model's ability to capture the intrinsic structure of the data was studied. Findings indicate that image restoration tasks significantly enhance semantic information extraction.

Keywords: Cataract; Deep learning; Hybrid model; Self-supervision.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Fig. 1
Fig. 1
Structure of NCME-Net.
Fig. 2
Fig. 2
The anterior-segment photographs of each category: (A) Normal; (B) Mild cataract; (C) Moderate cataract; (D) Severe cataract.
Fig. 3
Fig. 3
Structure of the decoder.
Fig. 4
Fig. 4
Image restoration results: (A) Normal; (B) Mild cataract; (C) Moderate cataract; (D) Severe cataract.
Fig. 5
Fig. 5
Structure of the DPCT block.
Fig. 6
Fig. 6
Other self-supervised pretraining tasks.
Fig. 7
Fig. 7
Results of the assessment of dichotomous indicators: (A) PPV; (B) NPV; (C) SE; (D) SP. Ori stands for ResNet50. 1/7 to 6/7 represent the performance of NCME-Net under different shielding rates.
Fig. 8
Fig. 8
Confusion matrices: (A) ResNet50; (B) 4/7; (C) 6/7. 0 for normal, 1 for mild cataracts, 2 for moderate cataracts, and 3 for severe cataracts.
Fig. 9
Fig. 9
Receiver operating characteristic curves: (A) Normal; (B) Mild cataract; (C) Moderate cataract; (D) Severe cataract.
See this image and copyright information in PMC

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