Principal component analysis based cataract grading and classification

Wu, Fan; Shen, Ruifang; Zhang, Qinyan; Yang, Ji-Jiang; Li, Jianqiang

doi:10.1109/healthcom.2015.7454545

Cited by 17 publications

(5 citation statements)

References 8 publications

(8 reference statements)

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“…Fan et al [14] presented a method utilizing PCA for reducing the feature dimensionality of fundus images obtained through wavelet and sketch-based techniques. The aim of this technique is to minimize computational demands while employing commonly utilized classification algorithms, such as SVMs, Bagging, DT, Gradient Boosting, and Random Forests (RF) to categorize cataracts.…”

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 of this approach is 81.52%. Fan et al [21] proposed a method that employs PCA to reduce the dimensionality of retrieved wavelet and sketched-based features from fundus images. This approach utilized widely used classification methods such as SVMs, bagging, random forests, gradient boosting, and decision trees in order to classify cataracts.…”

Section: Literature Workmentioning

confidence: 99%

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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“…Larger datasets with greater statistical power are required to assess the efficacy of the diagnostic measures. 98,99,125,126 The absence of longitudinal data limits our understanding of cataract development, treatment success, and the predictive value of diagnostic tests. 69,[127][128][129] Also, the research results can't be used with different groups of people because the images used to diagnose cataracts aren't varied enough.…”

Section: Summary Of Evidencementioning

confidence: 99%

An Assessment of Contemporary Methods and Data-Enabled Approaches for Early Cataract Detection

Muchibwa,

Eldaw,

Agyeman

2023

F1000Res

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Background: Cataracts are common causes of visual impairment. Preventing blindness requires an early and accurate diagnosis. This review examines current cataract diagnosis strategies, explores data-driven machine learning algorithms for early detection, investigates the use of artificial intelligence (AI) approaches, assesses improvements in cataract detection accuracy, identifies research gaps, and provides recommendations for future studies. Methods: We gathered labelled cataract and non-cataract fundus data from the Kaggle. Scholarly publications were sourced from reliable databases such as ProQuest, IEEE, ELSEVIER, Google Scholar, and PubMed. A detailed literature search with specific terms expanded the scope of this review. We included studies that used cataract and non-cataract fundus eye images from cross-sectional, retrospective, and prospective studies. The quality assessment used the AMSTAR tool, considering factors such as literature search comprehensiveness, study selection criteria, data extraction methodologies, and study validity (Table 1). Results: This study encompassed 130 research publications, focusing on machine learning models and clinical-based diagnostic approaches for early-stage cataract identification. The performance of machine-learning models is influenced by factors such as dataset noise and limited reliable data. Barriers to the successful implementation of AI for cataract diagnosis were identified. Conclusions: This review emphasises the obstacles hindering the broad application of AI in cataract diagnosis. Addressing these findings is vital for developing strategies to overcome these challenges and enhance cataract detection systems. To achieve improved accuracy and efficiency in cataract diagnosis, future research should prioritise efforts to enhance dataset availability and quality, reduce data noise, and refine machine-learning algorithms. Unlocking the full potential of AI and/or machine learning can lead to significant breakthroughs in cataract diagnosis, ultimately resulting in better patient outcomes and reduced visual impairments.

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Principal component analysis based cataract grading and classification

Cited by 17 publications

References 8 publications

Enhancing Cataract Detection Precision: A Deep Learning Approach

Enhancing Cataract Detection Precision: A Deep Learning Approach

Automatic Cataract Severity Detection and Grading Using Deep Learning

An Assessment of Contemporary Methods and Data-Enabled Approaches for Early Cataract Detection

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