Classification of Cataract Slit-Lamp Image Based on Machine Learning

Sigit, Riyanto; Satmoko, Maulana Bayu; Basuki, Dwi Kurnia; Si, Shaohui; M.Kom.,

doi:10.1109/isemantic.2018.8549701

Cited by 11 publications

(7 citation statements)

References 5 publications

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“…The cataract occurs when proteins are accumulated in different locations of the lens 2,6 which becomes cloudy. In terms of severity, vision becomes blurred with distressed colors and troubled with bright light, 5,6 which corresponds to vision impairment.…”

Section: Literature Reviewmentioning

confidence: 99%

“…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%

“…More than 50% of worldwide blindness cases are caused by the cataract where the number of blind people will exceed 40 million in 2025 2,3 . The cataract is involved due to smoking, genetics, alcohol, nutritional or metabolism disorders, medications or long exposure to sunlight 4–6 . In addition, it is correlated with several other pathologies 1,5 …”

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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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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“…In general, cataract diagnosis typically starts with a slit-lamp examination and is followed by physician analysis based on the slit-lamp image. The classification will be done based on the doctor's evaluation of the turbid area of the pupil [28]. The presence and severity of the cataract will be graded by comparing its appearance in slit-lamp images in contrast with a set of standard reference photographs.…”

Section: Automated Cataract Detection and Gradingmentioning

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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“…Simultaneously, the growth in functionality of machine-learning techniques has created an intersection point which may provide benefits in the optimization of distributed systems. Current ML-enhanced applications range from tourist guidance [9] to malware detection [10] to cataract detection [11], and similar approaches may provide substantial benefits in the energy market. In particular, ML approaches directed at implementation in embedded and/or "server-less" or "edge" computing systems [12] make the combination of ML and applications such as voltage optimization a very fertile environment for exploration.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Voltage Optimization Based on In-Band Sensors and Machine Learning

2019

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A feedback-based architecture is presented for the distribution grid which enables the use of Machine Learning (ML) techniques for various applications, including Dynamic Voltage Optimization (DVO) and Demand Response (DR). In this architecture, sensor devices are resident on the distribution grid and therefore have a unique awareness of multiple system parameters. This enables the use of ongoing ML techniques for implementation of critical applications in the Smart Grid. Monitoring devices are placed at the endpoints and monitoring/control devices are placed along the power line on various types of grid-resident systems. Because the devices are grid-resident and interact directly with other devices on the same physical link, applications such as ML-assisted DVO can be targeted with very high confidence.

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Classification of Cataract Slit-Lamp Image Based on Machine Learning

Cited by 11 publications

References 5 publications

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

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

Towards a Connected Mobile Cataract Screening System: A Future Approach

Dynamic Voltage Optimization Based on In-Band Sensors and Machine Learning

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