Fundus image based cataract classification

Zheng, Jiangbin; Guo, Liye; Peng, Lihui; Li, Jianqiang; Yang, Ji-Jiang; Liang, Qingfeng

doi:10.1109/ist.2014.6958452

Cited by 32 publications

(8 citation statements)

References 11 publications

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“…Zheng et al [13] proposed a method for classifying cataracts using 2-D DFT spectrograms of fundus images as classification features. To reduce the dimensionality of the feature vectors, the method employs principal component analysis (PCA) and trains and evaluates the LDA classifier using the Adaboost algorithm.…”

Section: Cataract Detection Using Conventional Methodsmentioning

confidence: 99%

Enhancing Cataract Detection Precision: A Deep Learning Approach

Yadav,

Yadav

2023

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Cataracts, characterized by the opacification of the eye lens leading to visual deterioration, pose a significant global health issue. Timely and accurate detection of cataracts is pivotal for halting disease progression and augmenting the patients' quality of life. However, conventional diagnostic approaches for cataract detection and grading rely heavily on the expertise of ophthalmologists, a solution that can be unduly costly and inaccessible for certain population segments seeking early intervention. Addressing this challenge, the present study introduces a computer-assisted diagnostic strategy for the detection and grading of cataracts, drawing on fundus retinal images. The proposed approach capitalizes on a deep convolutional neural network to extract features from fundus images, which are subsequently evaluated via three distinct classification algorithms: Support Vector Machine, Naive Bayes, and Decision Tree. The resultant categorization stratifies the images into four severity levels: mild, moderate, normal, and severe. Further enhancing the classifier's prediction accuracy, an Ensemble (ES) learning mechanism via a Majority Voting Scheme (MVS) process is incorporated into the study. A total of 1600 fundus images, sourced from various open-access databases and classified into four categories by an expert ophthalmologist, were utilized for the study. The proposed methodology demonstrated a commendable accuracy rate of 97.34% in the four-stage cataract classification and grading, outperforming existing methodologies. This research advances the field by introducing a reliable, cost-effective, and accessible solution for early cataract detection, contributing significantly to global health improvements.

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Section: Cataract Detection Using Conventional Methodsmentioning

confidence: 99%

Enhancing Cataract Detection Precision: A Deep Learning Approach

Yadav,

Yadav

2023

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“…The classification accuracy achieved by this method is 82.9%. 7 Zheng et al 8 suggested a method that utilized 2-D DFT features of retinal images for cataract classification. This method utilized PCA for dimensionality reduction of the feature vector, and the LDA classifier was trained and tested using the Adaboost algorithm.…”

Section: Related Workmentioning

confidence: 99%

Computer‐aided diagnosis of cataract severity using retinal fundus images and deep learning

Yadav

2022

Computational Intelligence

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“…Ophthalmic medical images have been widely employed in analyzing cataract severity and are expected to provide a better degree of accuracy in identifying and classifying cataract disease [10]. Generally, six types of ophthalmic images (i.e., slit-lamp images [11][12][13], retro illumination images [14], ultrasonic images [15,16], anterior segment optical coherence tomography images [17], fundus images [9,[18][19][20][21][22], and digital camera images [5,8,[23][24][25][26][27][28]) have been used for cataract detection. Fundus images and slit-lamp images are the two most frequently used in cataract detection.…”

Section: Related Workmentioning

confidence: 99%

The Use of Convolutional Neural Networks and Digital Camera Images in Cataract Detection

et al. 2022

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Cataract is one of the major causes of blindness in the world. Its early detection and treatment could greatly reduce the risk of deterioration and blindness. Instruments commonly used to detect cataracts are slit lamps and fundus cameras, which are highly expensive and require domain knowledge. Thus, the problem is that the lack of professional ophthalmologists could result in the delay of cataract detection, where medical treatment is inevitable. Therefore, this study aimed to design a convolutional neural network (CNN) with digital camera images (CNNDCI) system to detect cataracts efficiently and effectively. The designed CNNDCI system can perform the cataract identification process accurately in a user-friendly manner using smartphones to collect digital images. In addition, the existing numerical results provided by the literature were used to demonstrate the performance of the proposed CNNDCI system for cataract detection. Numerical results revealed that the designed CNNDCI system could identify cataracts effectively with satisfying accuracy. Thus, this study concluded that the presented CNNDCI architecture is a feasible and promising alternative for cataract detection.

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Fundus image based cataract classification

Cited by 32 publications

References 11 publications

Enhancing Cataract Detection Precision: A Deep Learning Approach

Enhancing Cataract Detection Precision: A Deep Learning Approach

Computer‐aided diagnosis of cataract severity using retinal fundus images and deep learning

The Use of Convolutional Neural Networks and Digital Camera Images in Cataract Detection

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