Automated segmentation and classifcation of retinal features for glaucoma diagnosis

Pathan, Sumaiya; Kumar, Preetham; Pai, Radhika M.; Bhandary, Sulatha V.

doi:10.1016/j.bspc.2020.102244

Cited by 38 publications

(14 citation statements)

References 37 publications

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“…Neither transfer learning nor finetuning were performed in these tests. Apart from state-of-the-art works [ 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 32 , 33 , 34 , 35 ] pretrained in several medical image datasets and ImageNet, our proposal received no extra training and only took the training partition of each presented dataset. Our source code is available on .…”

Section: Resultsmentioning

confidence: 99%

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NanoChest-Net: A Simple Convolutional Network for Radiological Studies Classification

et al. 2021

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The new coronavirus disease (COVID-19), pneumonia, tuberculosis, and breast cancer have one thing in common: these diseases can be diagnosed using radiological studies such as X-rays images. With radiological studies and technology, computer-aided diagnosis (CAD) results in a very useful technique to analyze and detect abnormalities using the images generated by X-ray machines. Some deep-learning techniques such as a convolutional neural network (CNN) can help physicians to obtain an effective pre-diagnosis. However, popular CNNs are enormous models and need a huge amount of data to obtain good results. In this paper, we introduce NanoChest-net, which is a small but effective CNN model that can be used to classify among different diseases using images from radiological studies. NanoChest-net proves to be effective in classifying among different diseases such as tuberculosis, pneumonia, and COVID-19. In two of the five datasets used in the experiments, NanoChest-net obtained the best results, while on the remaining datasets our model proved to be as good as baseline models from the state of the art such as the ResNet50, Xception, and DenseNet121. In addition, NanoChest-net is useful to classify radiological studies on the same level as state-of-the-art algorithms with the advantage that it does not require a large number of operations.

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Section: Resultsmentioning

confidence: 99%

“…CNNs are the most popular type of DL algorithms and the most used for medical image diagnosis. We can find several works from segmentation of lesions [ 13 , 14 , 15 ] to classification of different diseases [ 16 , 17 , 18 , 19 ].…”

Section: Introductionmentioning

confidence: 99%

NanoChest-Net: A Simple Convolutional Network for Radiological Studies Classification

et al. 2021

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“…e HRF dataset contains 30 images including 15 healthy and 15 glaucomatous images with a resolution of 3504 * 2336. Pathan et al used the HRF dataset in a real-time detection system and achieved a precision of 96.7% [29]. e Drishti database contains 101 images with a resolution of 2049 * 1757, which are used for analyzing glaucoma.…”

Section: Databasesmentioning

confidence: 99%

A Comprehensive Convolutional Neural Network Survey to Detect Glaucoma Disease

Zafar

Aamir

Nawi

et al. 2022

Mobile Information Systems

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Due to recent advances in computer technology and the accessibility of large datasets, deep learning has become at the forefront of artificial intelligence and on various tasks, especially those related to image classification and modelling, its performance is often equal to or even better than human appreciation. Ophthalmology has always been in an ideal position to employ one of CNN’s most popular deep learning algorithms to evaluate vast volumes of data from these tests since it is one of the health practices that focuses extensively on imaging. Glaucoma is one among the situations in which CNN can benefit from the enormous quantity of data collected by tests that assess the anatomy as well as function of the optic nerve and macula. We discussed the recommended use of CNN algorithm for specific glaucoma scenarios, such as fundus photography screening and diagnosis and detection of the course of glaucoma through OCT imaging modality. The purpose of this article is not only to critically examine and discuss the latest applications of CNN models in glaucoma but also to focus on the challenges associated with developing such models for screening, diagnosis, and progress detection. After a brief overview of the clinical practices and their comparison with conventional clinical methods, we discussed training and validation of CNN algorithm and how it was developed and why it is particularly suitable for glaucoma. The following features make our contribution worthwhile and unique among the reviews of similar kind: (i) our review classifies the existing literature with respect to detection of the glaucoma disease using conventional and nonconventional approaches; (ii) it covers a very different outlook of the glaucoma disease by providing in-depth discussions of the existing works at different granularity levels, that is, from primary to mediatory to the severe level; (iii) this state-of-the-art review covers each article in the following dimensions: the computer-based approach to tomographic model; analysis of different datasets; and summarizing the literature review in a disciplined way by mentioning the research gap concluded with discussion on future work.

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“…[22] For instance, building glaucoma interaction networks [17], assessing the optic disk [23,24], and detecting visual field progression [19][20][21], diagnosis, and screening. [10,11,[25][26][27][28][29][30][31][32][33].…”

Section: Literature Reviewmentioning

confidence: 99%

Development of glaucoma predictive model and risk factors assessment based on supervised models

et al. 2021

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Objectives To develop and to propose a machine learning model for predicting glaucoma and identifying its risk factors. Method Data analysis pipeline is designed for this study based on Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology. The main steps of the pipeline include data sampling, preprocessing, classification and evaluation and validation. Data sampling for providing the training dataset was performed with balanced sampling based on over-sampling and under-sampling methods. Data preprocessing steps were missing value imputation and normalization. For classification step, several machine learning models were designed for predicting glaucoma including Decision Trees (DTs), K-Nearest Neighbors (K-NN), Support Vector Machines (SVM), Random Forests (RFs), Extra Trees (ETs) and Bagging Ensemble methods. Moreover, in the classification step, a novel stacking ensemble model is designed and proposed using the superior classifiers. Results The data were from Shahroud Eye Cohort Study including demographic and ophthalmology data for 5190 participants aged 40-64 living in Shahroud, northeast Iran. The main variables considered in this dataset were 67 demographics, ophthalmologic, optometric, perimetry, and biometry features for 4561 people, including 4474 non-glaucoma participants and 87 glaucoma patients. Experimental results show that DTs and RFs trained based on under-sampling of the training dataset have superior performance for predicting glaucoma than the compared single classifiers and bagging ensemble methods with the average accuracy of 87.61 and 88.87, the sensitivity of 73.80 and 72.35, specificity of 87.88 and 89.10 and area under the curve (AUC) of 91.04 and 94.53, respectively. The proposed stacking ensemble has an average accuracy of 83.56, a sensitivity of 82.21, a specificity of 81.32, and an AUC of 88.54. Conclusions In this study, a machine learning model is proposed and developed to predict glaucoma disease among persons aged 40-64. Top predictors in this study considered features for discriminating and predicting non-glaucoma persons from glaucoma patients include the number of the visual field detect on perimetry, vertical cup to disk ratio, white to white diameter, systolic blood pressure, pupil barycenter on Y coordinate, age, and axial length.

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Automated segmentation and classifcation of retinal features for glaucoma diagnosis

Cited by 38 publications

References 37 publications

NanoChest-Net: A Simple Convolutional Network for Radiological Studies Classification

NanoChest-Net: A Simple Convolutional Network for Radiological Studies Classification

A Comprehensive Convolutional Neural Network Survey to Detect Glaucoma Disease

Development of glaucoma predictive model and risk factors assessment based on supervised models

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