Automated cataract disease detection on anterior segment eye images using adaptive thresholding and fine tuned inception-v3 model

Faizal, Sahil; Rajput, Charu Anant; Tripathi, Rupali; Verma, Bhumika; Prusty, Manas Ranjan; Korade, Shivani Sachin

doi:10.1016/j.bspc.2022.104550

Cited by 16 publications

(4 citation statements)

References 19 publications

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“…Likewise, Faizal et al [16] presented an algorithm designed to aid in the automated detection of cataracts, highlighting the relevance of identifying them early to prevent vision loss. They employed an enhanced CNN model trained with visible wavelength images and validated with medical images of the anterior segment of the eye.…”

Section: Leterature Reviewmentioning

confidence: 99%

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: Leterature Reviewmentioning

confidence: 99%

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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“…Inception-v3 is a CNN designed for object recognition in images. It uses a combination of filter layers of different sizes and depths to capture features at different scales and levels of abstraction in the input images [36] [37]. The architecture, as depicted in Fig.…”

Section: K Inception-v3mentioning

confidence: 99%

Disease Identification in Crop Plants based on Convolutional Neural Networks

Iparraguirre-Villanueva¹,

Guevara-Ponce²,

Torres-Ceclén³

et al. 2023

IJACSA

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The identification, classification and treatment of crop plant diseases are essential for agricultural production. Some of the most common diseases include root rot, powdery mildew, mosaic, leaf spot and fruit rot. Machine learning (ML) technology and convolutional neural networks (CNN) have proven to be very useful in this field. This work aims to identify and classify diseases in crop plants, from the data set obtained from Plant Village, with images of diseased plant leaves and their corresponding Tags, using CNN with transfer learning. For processing, the dataset composing of more than 87 thousand images, divided into 38 classes and 26 disease types, was used. Three CNN models (DenseNet-201, ResNet-50 and Inception-v3) were used to identify and classify the images. The results showed that the DenseNet-201 and Inception-v3 models achieved an accuracy of 98% in plant disease identification and classification, slightly higher than the ResNet-50 model, which achieved an accuracy of 97%, thus demonstrating an effective and promising approach, being able to learn relevant features from the images and classify them accurately. Overall, ML in conjunction with CNNs proved to be an effective tool for identifying and classifying diseases in crop plants. The CNN models used in this work are a very good choice for this type of tasks, since they proved to have a very high performance in classification tasks. In terms of accuracy, all three models are very accurate in image classification, with an accuracy of over 96% with large data sets.

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“…Slit-lamp images provides high-resolution images of the eye's anterior segment, enabling detailed examination of the lens and other structures. [41][42][43][44][45][46] It allows the precise localisation and characterisation of different types of cataracts based on their position within the lens. Additionally, the dynamic adjustment of light and slit orientation permits various perspectives and lighting conditions for viewing cataracts in real-time.…”

Section: Eligibility Criteriamentioning

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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Automated cataract disease detection on anterior segment eye images using adaptive thresholding and fine tuned inception-v3 model

Cited by 16 publications

References 19 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

Disease Identification in Crop Plants based on Convolutional Neural Networks

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

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