Fundus image-based automatic cataract detection and grading system

Varma, Neha; Yadav, Sunita; Yadav, Jay Kant Pratap Singh

doi:10.1063/5.0129359

Cited by 3 publications

(3 citation statements)

References 13 publications

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“…The results presented in studies [14], [17]- [19], offer valuable insights into the development and effectiveness of algorithms and techniques in the field of eye disease detection and visual health assessment. In the following, these results are discussed and compared with those of this research.…”

Section: Discussionmentioning

confidence: 99%

“…However, the location of the staphyloma apex had an average accuracy of 1.35 ± 1.34 mm, which could affect the accurate identification of ocular diseases. On the other hand, Varma et al [17] used deep learning to create a cataract diagnostic system with an impressive 92.7% accuracy, surpassing previous techniques. This highlights the effectiveness of deep learning in early cataract identification, crucial for timely diagnosis and treatment.…”

Section: Discussionmentioning

confidence: 99%

“…They employed an enhanced CNN model trained with visible wavelength images and validated with medical images of the anterior segment of the eye. The proposed algorithm achieves a high classification accuracy of approximately Similarly, for the purpose of creating an early cataract diagnosis system, Varma et al [17] used a deep learning-based CNN architecture. The technique uses fundus image analysis to accurately classify cataracts into stages of mild, moderate, none, and severe severity (mild, moderate, none, and severe).…”

Section: Leterature Reviewmentioning

confidence: 99%

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Advances in the diagnosis of ocular diseases: an innovative approach through an expert system

Andrade-Arenas,

Yactayo-Arias

2024

Bulletin EEI

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In the context of ophthalmic care, where early diagnosis of eye disorders plays a crucial role in patients' quality of life, this study focused on the development and evaluation of an expert system based on SWI Prolog. The main objective of this research was to provide an effective method for the preliminary diagnosis of ocular disorders, including cataract, trachoma, uveitis, glaucoma, and presbyopia. For the evaluation of the system, a confusion matrix was implemented and accuracy, sensitivity and specificity were calculated using a sample of 30 cases, of which 20 were positive and 10 negatives. The findings revealed an outstanding accuracy of 95%, with a sensitivity and specificity of 90%. This highlights the potential of the tool as an effective means of early detection of visual problems. In conclusion, this expert system represents a significant advance in ophthalmologic diagnosis, with important implications for clinical care and patients' quality of life, although expansion and validation of the tool in further clinical studies is suggested for its wider and more successful implementation in the field of ophthalmology.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Leterature Reviewmentioning

confidence: 99%

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Advances in the diagnosis of ocular diseases: an innovative approach through an expert system

Andrade-Arenas,

Yactayo-Arias

2024

Bulletin EEI

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A novel CatractNetDetect deep learning model for effective cataract classification through data fusion of fundus images

Ismail,

Alsalamah

2024

Discov Artif Intell

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Automatic Cataract Severity Detection and Grading Using Deep Learning

Yadav

2023

Journal of Sensors

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Cataracts are an eye condition that causes the eye’s lens to become cloudy and is a significant cause of vision loss worldwide. Accurate and timely detection and diagnosis of cataracts can prevent vision loss. However, poor medical care and expensive treatments prevented cataract patients from receiving appropriate treatment on time. Therefore, an inexpensive system that diagnoses cataracts at an early stage needs to be developed. This study proposes an automatic method for detecting and classifying cataracts in their earliest stages by combining a deep learning (DL) model with the 2D-discrete Fourier transform (DFT) spectrum of fundus images. The proposed method calculates the spectrogram of fundus images using a 2D-DFT and uses this calculated spectrogram as an input to the DL model for feature extraction. After feature extraction, the classification task is performed by a softmax classifier. This study collected fundus images from various open-source databases that are freely available on the Internet and classified them into four classes based on an ophthalmologist’s assessment. All the collected fundus images from various datasets with open access are unsuitable for cataract diagnosis. Consequently, a module for identifying the fundus images of good and poor quality is also incorporated into this method. The experimental results show that the proposed system can outperform previous state-of-the-art works by a significant margin compared to a benchmark of four-class accuracy and achieves the four-class accuracy of 93.10%.

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Fundus image-based automatic cataract detection and grading system

Cited by 3 publications

References 13 publications

Advances in the diagnosis of ocular diseases: an innovative approach through an expert system

Advances in the diagnosis of ocular diseases: an innovative approach through an expert system

A novel CatractNetDetect deep learning model for effective cataract classification through data fusion of fundus images

Automatic Cataract Severity Detection and Grading Using Deep Learning

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