Performance of deep transfer learning for detecting abnormal fundus images

Yu, Yi; Chen, Xiao; Zhu, Xing; Zhang, Peng-Fei; Hou, Yinfen; Zhang, Rongrong; Wu, Changfan

doi:10.4103/joco.joco_123_20

“…2,3,[5][6][7] The use of AI technologies has the potential to increase the chance of an early diagnosis of cataracts, 5,6,8,9 reduce undetected instances or features linked to cataracts (e.g., change in retinal veins and artery size and structure, shape of cataract formation on the lens, colour change of the lens disc, uneven corneal surface, leukocoria, or red reflex appearance in the pupil), and allow time for treatment before the cataract is at an irreversible severe stage, thereby improving community health. [9][10][11][12][13][14][15][16][17] Numerous AI approaches have been developed to address the challenges associated with cataracts, such as limited-service availability, mistakes, and misdiagnoses. Both clinical and AI-based techniques have been proposed for computerised cataract diagnosis and treatment.…”

Section: Conceptsmentioning

confidence: 99%

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

Muchibwa,

Eldaw,

Agyeman

2023

F1000Res

0

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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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An Efficient Transfer Learning Based Approach for Detecting the Abnormal Fundus Images

Joshi

¹

,

Masilamani

²

2021

2021 5th Conference on Information and Communication Technology (CICT)

4

0

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

Muchibwa,

Eldaw,

Agyeman

2024

F1000Res

0

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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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Performance of deep transfer learning for detecting abnormal fundus images

Cited by 5 publications

References 22 publications

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

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

An Efficient Transfer Learning Based Approach for Detecting the Abnormal Fundus Images

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

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