A Robust Automated Cataract Detection Algorithm Using Diagnostic Opinion Based Parameter Thresholding for Telemedicine Application

Pathak, Shashwat; Kumar, Basant

doi:10.3390/electronics5030057

Cited by 32 publications

(11 citation statements)

References 13 publications

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“…Similarly, various cataract detection and grading methods are presented in Table 1 with their comparative limitations and system complexities to highlight the significance of proposed system. Current work is extension to our earlier work [20] with added feature of grading of cataracts and development of IoT based device and a cloud computing based platform for mass detection of cataracts.…”

Section: Literature Survey Of Image Processing Based Cataract Detecti...mentioning

confidence: 97%

Development of portable and robust cataract detection and grading system by analyzing multiple texture features for Tele-Ophthalmology

Pathak¹,

Raj

Singh³

et al. 2022

Multimed Tools Appl

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This paper presents a low cost, robust, portable and automated cataract detection system which can detect the presence of cataract from the colored digital eye images and grade their severity. Ophthalmologists detect cataract through visual screening using ophthalmoscope and slit lamps. Conventionally a patient has to visit an ophthalmologist for eye screening and treatment follows the course. Developing countries lack the proper health infrastructure and face huge scarcity of trained medical professionals as well as technicians. The condition is not very satisfactory with the rural and remote areas of developed nations. To bridge this barrier between the patient and the availability of resources, current work focuses on the development of portable low-cost, robust cataract screening and grading system. Similar works use fundus and retinal images which use costly imaging modules and image based detection algorithms which use much complex neural network models. Current work derives its benefit from the advancements in digital image processing techniques. A set of preprocessing has been done on the colored eye image and later texture information in form of mean intensity, uniformity, standard deviation and randomness has been calculated and mapped with the diagnostic opinion of doctor for cataract screening of over 200 patients. For different grades of cataract severity edge

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Section: Literature Survey Of Image Processing Based Cataract Detecti...mentioning

confidence: 97%

Development of portable and robust cataract detection and grading system by analyzing multiple texture features for Tele-Ophthalmology

Pathak¹,

Raj

Singh³

et al. 2022

Multimed Tools Appl

Self Cite

View full text Add to dashboard Cite

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“…Prior work has presented smartphone apps to diagnose disease based on eye-images, e.g. for cataract detection [20,21], to identify high cholesterol levels [22,23], to diagnose concussions [24] and for glaucoma screening [23,25]. Akil and Elloumi [26] present a meta paper, investigating the image quality and diagnosis performance achieved in eight prior works using smartphones equipped with additional lenses for retinal examination.…”

Section: A Smartphone-based Eye Disease Diagnosismentioning

confidence: 99%

The Usability and Trustworthiness of Medical Eye Images

Diethei¹,

Colley²,

Dannenberg³

et al. 2021

Preprint

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The majority of blindness is preventable, and is located in developing countries. While mHealth applications for retinal imaging in combination with affordable smartphone lens adaptors are a step towards better eye care access, the expert knowledge and additional hardware needed are often unavailable in developing countries. Eye screening apps without lens adaptors exist, but we do not know much about the experience of guiding users to take medical eye images. Additionally, when an AI based diagnosis is provided, trust plays an important role in ensuring in the adoption. This work addresses factors that impact the usability and trustworthiness dimensions of mHealth applications. We present the design, development and evaluation of EyeGuide, a mobile app that assists users in taking medical eye images using only their smartphone camera. In a study (n=28) we observed that users of an interactive tutorial captured images faster compared to audible tone based guidance. In a second study (n=40) we found out that providing disease-specific background information was the most effective factor to increase trustworthiness in the AI based diagnosis. Application areas of EyeGuide are AI based disease detection and telemedicine examinations.

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“…Four ML classifiers, namely, multilayer perceptron (MLP), support vector machine (SVM), random forest (RF), and J48, were employed on a liver cancer fused dataset. The MLP classifiers performed best among the implemented classifiers because MLP mostly performed well for noisy, big, and complex data [35]. The MLP classifiers [36,37] are explained below; the production of input weight and bias are summed up using the summation function (ρ n ) given in Equation (29).…”

Section: Classificationmentioning

confidence: 99%

Machine-Learning Based Hybrid-Feature Analysis for Liver Cancer Classification Using Fused (MR and CT) Images

et al. 2020

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The purpose of this research is to demonstrate the ability of machine-learning (ML) methods for liver cancer classification using a fused dataset of two-dimensional (2D) computed tomography (CT) scans and magnetic resonance imaging (MRI). Datasets of benign (hepatocellular adenoma, hemangioma, cyst) and malignant (hepatocellular carcinoma, hepatoblastoma, metastasis) liver cancer were acquired at Bahawal Victoria Hospital (BVH), Bahawalpur, Pakistan. The final dataset was generated by fusion of 1200 (100 × 6 × 2) MR and CT-scan images, 200 (100 MRI and 100 CT-scan) images size 512 × 512 for each class of cancer. The acquired dataset was preprocessed by employing the Gabor filters to reduce the noise and taking an automated region of interest (ROIs) using an Otsu thresholding-based segmentation approach. The preprocessed dataset was used to acquire 254 hybrid-feature data for each ROI, which is the combination of the histogram, wavelet, co-occurrence, and run-length features, while 10 optimized hybrid features were selected by employing (probability of error plus average correlation) feature selection technique. For classification, we deployed this optimized hybrid-feature dataset to four ML classifiers: multilayer perceptron (MLP), support vector machine (SVM), random forest (RF), and J48, using a ten fold cross-validation method. MLP showed an overall accuracy of (95.78% on MRI and 97.44% on CT). Unfortunately, the obtained results were not promising, and there were some limitations due to the different modalities of the dataset. Thereafter, a fusion of MRI and CT-scan datasets generated the fused optimized hybrid-feature dataset. The MLP has shown a promising accuracy of 99% among all the deployed classifiers.

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A Robust Automated Cataract Detection Algorithm Using Diagnostic Opinion Based Parameter Thresholding for Telemedicine Application

Cited by 32 publications

References 13 publications

Development of portable and robust cataract detection and grading system by analyzing multiple texture features for Tele-Ophthalmology

Development of portable and robust cataract detection and grading system by analyzing multiple texture features for Tele-Ophthalmology

The Usability and Trustworthiness of Medical Eye Images

Machine-Learning Based Hybrid-Feature Analysis for Liver Cancer Classification Using Fused (MR and CT) Images

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