A reliable automatic cataract detection using deep learning

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

doi:10.1007/s13198-023-01923-2

Cited by 9 publications

(2 citation statements)

References 30 publications

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“…Similarly, Varma et al [20] created an automatic cataract diagnosis and classification system using fundus images. A deep convolutional neural network (DCNN) is employed to automatically extract features from these images and then used to classify cataract severity in four stages.…”

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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“…The eye is prone to various disorders, notably cataracts, causing vision loss if untreated [1,2]. Factors like aging, diabetes, UV radiation, genetics, pollutants, eye trauma, and habits contribute to cataracts [3]. Diagnosis involves a comprehensive eye exam, and treatment includes removing the clouded lens and implanting an artificial one.…”

Section: Introductionmentioning

confidence: 99%

CSDNet: A Novel Deep Learning Framework for Improved Cataract State Detection

P.L,

Vaddi,

Elish

et al. 2024

Diagnostics

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Cataracts, known for lens clouding and being a common cause of visual impairment, persist as a primary contributor to vision loss and blindness, presenting notable diagnostic and prognostic challenges. This work presents a novel framework called the Cataract States Detection Network (CSDNet), which utilizes deep learning methods to improve the detection of cataract states. The aim is to create a framework that is more lightweight and adaptable for use in environments or devices with limited memory or storage capacity. This involves reducing the number of trainable parameters while still allowing for effective learning of representations from data. Additionally, the framework is designed to be suitable for real-time or near-real-time applications where rapid inference is essential. This study utilizes cataract and normal images from the Ocular Disease Intelligent Recognition (ODIR) database. The suggested model employs smaller kernels, fewer training parameters, and layers to efficiently decrease the number of trainable parameters, thereby lowering computational costs and average running time compared to other pre-trained models such as VGG19, ResNet50, DenseNet201, MIRNet, Inception V3, Xception, and Efficient net B0. The experimental results illustrate that the proposed approach achieves a binary classification accuracy of 97.24% (normal or cataract) and an average cataract state detection accuracy of 98.17% (normal, grade 1—minimal cloudiness, grade 2—immature cataract, grade 3—mature cataract, and grade 4—hyper mature cataract), competing with state-of-the-art cataract detection methods. The resulting model is lightweight at 17 MB and has fewer trainable parameters (175, 617), making it suitable for deployment in environments or devices with constrained memory or storage capacity. With a runtime of 212 ms, it is well-suited for real-time or near-real-time applications requiring rapid inference.

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Cloud-Based Cataract Recognition System Using Hybrid Classifier Model

Rajavel,

Nagappan

2024

Lecture Notes in Electrical Engineering

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A reliable automatic cataract detection using deep learning

Cited by 9 publications

References 30 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

CSDNet: A Novel Deep Learning Framework for Improved Cataract State Detection

Cloud-Based Cataract Recognition System Using Hybrid Classifier Model

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