Mobile Application Based Cataract Detection System

Agarwal, Vaibhav; Gupta, Vaibhav; Vashisht, Vivasvan Manasvi; Sharma, Kiran; Sharma, Neetu

doi:10.1109/icoei.2019.8862774

Cited by 21 publications

(14 citation statements)

References 14 publications

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“…Agarwal, Gupta, Vashisht, Sharma, & Sharma [62] also proposed smartphone-based Android applications that were developed using the proposed methodology and can be used for cataract detection. They utilised the combination of machine learning and image processing techniques in their study to develop the proposed mobile applications.…”

Section: Modern Trends In Cataract Screeningmentioning

confidence: 99%

Towards a Connected Mobile Cataract Screening System: A Future Approach

et al. 2022

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Advances in computing and AI technology have promoted the development of connected health systems, indirectly influencing approaches to cataract treatment. In addition, thanks to the development of methods for cataract detection and grading using different imaging modalities, ophthalmologists can make diagnoses with significant objectivity. This paper aims to review the development and limitations of published methods for cataract detection and grading using different imaging modalities. Over the years, the proposed methods have shown significant improvement and reasonable effort towards automated cataract detection and grading systems that utilise various imaging modalities, such as optical coherence tomography (OCT), fundus, and slit-lamp images. However, more robust and fully automated cataract detection and grading systems are still needed. In addition, imaging modalities such as fundus, slit-lamps, and OCT images require medical equipment that is expensive and not portable. Therefore, the use of digital images from a smartphone as the future of cataract screening tools could be a practical and helpful solution for ophthalmologists, especially in rural areas with limited healthcare facilities.

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Section: Modern Trends In Cataract Screeningmentioning

confidence: 99%

Towards a Connected Mobile Cataract Screening System: A Future Approach

et al. 2022

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“…2,3 The cataract is involved due to smoking, genetics, alcohol, nutritional or metabolism disorders, medications or long exposure to sunlight. [4][5][6] In addition, it is correlated with several other pathologies. 1,5 The cataract can be diagnosed through slit-lamp photography, which consists of reflecting light inside the lens structure and deducing the cataract disease from the non-uniform illumination of the refracted intensity.…”

Section: Introductionmentioning

confidence: 99%

“…[4][5][6] In addition, it is correlated with several other pathologies. 1,5 The cataract can be diagnosed through slit-lamp photography, which consists of reflecting light inside the lens structure and deducing the cataract disease from the non-uniform illumination of the refracted intensity. The Lens Opacities Classification System III, the American Cooperative Cataract Research Group protocol and the Oxford Clinical Cataract Classification are the wellknown protocols for classifying the cataract disease, which is based on convoluted procedures and require well-experienced ophthalmologists.…”

Section: Introductionmentioning

confidence: 99%

“…While the cataract develops slowly, it is hard to recognize it since its symptoms are similar to poor visual acuity. 3,5 Indeed, it is always diagnosed through a visual acuity test. This pathology imposes limiting activities and affects the life quality.…”

Section: Introductionmentioning

confidence: 99%

“…This pathology imposes limiting activities and affects the life quality. 1,5 Therefore, it is recommended to make a periodical diagnosis in order to enhance the life quality of cataract-affected patients. 3 However, the process of cataract diagnosis is time-consuming and leads to an important workload, especially with the higher rate of requested diagnosis.…”

Section: Introductionmentioning

confidence: 99%

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Cataract grading method based on deep convolutional neural networks and stacking ensemble learning

Elloumi

2022

Int J Imaging Syst Tech

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The cataract is the most common cause of severe vision impairment or blindness worldwide. A periodical diagnosis is recommended in order to prevent cataract severity, where screening might be feasibly ensured through fundus images. In this paper, we propose a CAD system to efficiently grade the cataract from fundus images. For such a need, an ensemble learning framework is put forward where the knowledge of three convolutional deep neural networks is stacked in order to perform an efficient cataract prediction. The main contributions of this work are given as follows: (1) Preprocessing and data augmentation of fundus images are carried out to ensure the robustness of the cataract grading; (2) The well‐known DL architectures (Inception‐V3, MobileNet‐V2 and NasNet‐Mobile) are fine‐tuned and learned as base classifiers; (3) An Extreme Learning Machine (ELM) is performed as a meta classifier to stack the predictions of base classifiers. The evaluation is conducted using a dataset of 590 fundus images selected from two public databases. The suggested framework achieves 94.07% accuracy, 94.37% sensitivity, 93.62% specificity, 95.71% precision and 95.04% F‐measure for cataract grading. The experimentation proves that the proposed framework successfully grades fundus images into cataract severity. Moreover, stacking ensemble learning allows achieving a performance that significantly surpasses the ones realized by each DL architecture, applied separately.

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