Cataract detection using smartphone

Rana, Juyel; Galib, Syed Md.

doi:10.1109/eict.2017.8275136

Cited by 20 publications

(17 citation statements)

References 6 publications

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“…To distinguish healthy from diseased eyes, 10-fold cross-validation and the SVM classifier were adopted for the classification task. Previous methods of detecting cataracts using smartphones include the GLCM method proposed by Fuadeh et al, [11] the retinal exam method proposed by Lau et al and Kaur et al, [13,14] and the RGB color-based texture extraction method proposed by Rana et al [15].…”

Section: Discussionmentioning

confidence: 99%

Detecting Cataract Using Smartphones

Askarian

Chong³

2021

IEEE J. Transl. Eng. Health Med.

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Objective: Cataract, which is the clouding of the crystalline lens, is the most prevalent eye disease accounting for 51% of all eye diseases in the U.S. Cataract is a progressive disease, and its early detection is critical for preventing blindness. In this paper, an efficient approach to identify cataract disease by adopting luminance features using a smartphone is proposed. Methods: Initially, eye images captured by a smartphone were cropped to extract the lens, and the images were preprocessed to remove irrelevant background and noise by utilizing median filter and watershed transformation. Then, a novel luminance transformation from pixel brightness algorithm was introduced to extract lens image features. The luminance and texture features of different types of cataract disease images could be obtained accurately in this stage. Finally, by adopting support vector machines (SVM) as the classification method, cataract eyes were identified. Results: From all the images that we fed into our system, our method could diagnose diseased eyes with 96.6% accuracy, 93.4% specificity, and 93.75% sensitivity. Conclusion: The proposed method provides an affordable, rapid, easy-to-use, and versatile method for detecting cataracts by using smartphones without the use of bulky and expensive imaging devices. This method can be used for bedside telemedicine applications or in remote areas that have medical shortages. Previous smartphone-based cataract detection methods include texture feature analysis with 95 % accuracy, Gray Level Co-occurrence Matrix (GLCM) method with 89% accuracy, red reflex measurement method, and RGB color feature extraction method using cascade classifier with 90% accuracy. The accuracy of cataract detection in these studies is subject to changes in smartphone models and/or environmental conditions. However, our novel luminance-based method copes with different smartphone camera sensors and chroma variations, while operating independently from sensors' color characteristics and changes in distances and camera angle. Clinical and TranslationalImpact-This study is an early/pre-clinical research proposing a novel luminance-based method of detecting cataract using smartphones for remote/at-home monitoring and telemedicine application.

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Section: Discussionmentioning

confidence: 99%

Detecting Cataract Using Smartphones

Askarian

Chong³

2021

IEEE J. Transl. Eng. Health Med.

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“…In [31], the fundus images were classified as cataract images using an SVM classifier, and then an RBF Network graded their severity with the specificity of 93.33%. Rana and Galib [1] proposed a mobile application on a smartphone (Android, iOS, Windows) that enabled the public to carry out self-screening cataract detection. They considered the texture information and reported 85% efficiency.…”

Section: A Machine Learning-based Methodsmentioning

confidence: 99%

“…It is a worldwide leading eye disease that develops gradually and does not affect sight early. However, after a while, it can interfere with vision and even cause vision loss in people over age 40 [1]. Cataract detection in earlier stages may avoid painful and costly surgeries and prevent blindness depending on its severity [2].…”

Section: Introductionmentioning

confidence: 99%

CataractNet: An Automated Cataract Detection System Using Deep Learning for Fundus Images

Junayed

Islam²,

Sadeghzadeh

et al. 2021

IEEE Access

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Cataract is one of the most common eye disorders that causes vision distortion. Accurate and timely detection of cataracts is the best way to control the risk and avoid blindness. Recently, artificial intelligence-based cataract detection systems have been received research attention. In this paper, a novel deep neural network, namely CataractNet, is proposed for automatic cataract detection in fundus images. The loss and activation functions are tuned to train the network with small kernels, fewer training parameters, and layers. Thus, the computational cost and average running time of CataractNet are significantly reduced compared to other pre-trained Convolutional Neural Network (CNN) models. The proposed network is optimized with the Adam optimizer. A total of 1130 cataract and non-cataract fundus images were collected and augmented to 4746 images to train the model. For avoiding the over-fitting problem, the dataset is extended through augmentation before model training. Experimental results prove that the proposed method outperforms the state-of-the-art cataract detection approaches with an average accuracy of 99.13%.

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“…From a public health perspective, 32 million people suffer from total blindness or severe visual impairment owing to anterior segment diseases globally, such as cataract, refractive errors, corneal opacities, and infectious diseases. [4][5][6][7][8][9] Telemedicine-enabled portable digital slit lamps can help to decentralize screening to close-to-patient contexts such as public health centers, primary care centers, pharmacies, and even in the convenience of a patient's home, as may be warranted during the case of the current coronavirus disease 2019 (COVID-19) pandemic. Simpler, cost-effective, and ergonomically designed portable digital slit lamps can thus help to decentralize the screening and management of anterior segment diseases.…”

Section: Introductionmentioning

confidence: 99%

“…This feature includes remote synchronous and asynchronous teleconsultations for regular eye care, postsurgical monitoring as well as in emergency care contexts. 4,[6][7][8][9] In this study, we report a novel design for a portable digital slit lamp (PSL D20) that works on an advanced optical design and documents images instantly with the help of a smartphone. We have focused specifically on design ergonomics to make it usable by nonspecialized health care workers, with minimum training.…”

Section: Introductionmentioning

confidence: 99%

Design and Performance Characterization of a Novel, Smartphone-Based, Portable Digital Slit Lamp for Anterior Segment Screening Using Telemedicine

Dutt

Nagarajan

Vadivel

et al. 2021

Trans. Vis. Sci. Tech.

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Telemedicine-enabled, portable digital slit lamps can help to decentralize screening to close-to-patient contexts. We report a novel design for a portable, digital slit lamp using a smartphone. It works on an advanced optical design and has the capability of instantaneous, objective photodocumentation to capture anterior segment images and is telemedicine-enabled. Methods:The device is constructed keeping its usability and the importance of design ergonomics for nonspecialized field personnel in mind. The optical design is described, and the resolution and magnification are compared with traditional desktop-based slit lamps. A Health Insurance Portability and Accountability Act (HIPAA)-compliant, patient management software is integrated to synchronize the captured images with a secure cloud server along with a sharpness algorithm to extract the best focused frames of the cornea, iris, and lens, from videos. We demonstrate its photodocumentation ability and teleophthalmology feasibility by capturing images in a pilot study from nine subjects.Results: Images were obtained in various illumination, magnification, and filter settings. Synchronous and asynchronous teleophthalmology consults were conducted. The performance of the device was shown to be limited by the smartphone sensor resolution and not the optical design, because the Air Force target resolution was found to be the same on smartphone-mounted traditional slit lamps despite a lower magnification. Conclusions:The novel, portable, digital slit lamp with advanced optical design using smartphones has the ability to screen for anterior segment pathologies using telemedicine.Translational Relevance: A portable, telemedicine-friendly, ergonomically designed, slit lamp used by nonspecialist personnel allows for both synchronous and asynchronous modes of consultation at remote locations, facilitating mass screening programs.

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Cataract detection using smartphone

Cited by 20 publications

References 6 publications

Detecting Cataract Using Smartphones

Detecting Cataract Using Smartphones

CataractNet: An Automated Cataract Detection System Using Deep Learning for Fundus Images

Design and Performance Characterization of a Novel, Smartphone-Based, Portable Digital Slit Lamp for Anterior Segment Screening Using Telemedicine

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