Robust keratoconus detection with Bayesian network classifier for Placido-based corneal indices

Castro-Luna, Gracia; Martínez–Finkelshtein, Andrei; Ramos-López, Darío

doi:10.1016/j.clae.2019.12.006

Cited by 19 publications

(29 citation statements)

References 33 publications

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“…They analyzed the patients’ multilayer perceptron in their database, obtaining sensitivity 90.00%, specificity 90.00% and AUC 0.96 to differentiate subclinical and normal keratoconus. In 2019, Castro-Luna et al [ 25 ] used Bayesian classifiers, obtaining a sensitivity, specificity, precision and accuracy of 100% to differentiate normal corneas and keratoconus. Issarti et al [ 26 ] developed a mathematical model called CAD (computer-aided diagnosis) for early keratoconus detection using feedforward neural network combined with Grossberg-Runge-Kutta architecture (feedforward neural network and a Grossberg-Runge Kutta architecture).…”

Section: Discussionmentioning

confidence: 99%

Diagnosis of Subclinical Keratoconus Based on Machine Learning Techniques

Castro-Luna

Jiménez-Rodríguez

Castaño-Fernández

et al. 2021

JCM

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(1) Background: Keratoconus is a non-inflammatory corneal disease characterized by gradual thinning of the stroma, resulting in irreversible visual quality and quantity decline. Early detection of keratoconus and subsequent prevention of possible risks are crucial factors in its progression. Random forest is a machine learning technique for classification based on the construction of thousands of decision trees. The aim of this study was to use the random forest technique in the classification and prediction of subclinical keratoconus, considering the metrics proposed by Pentacam and Corvis. (2) Methods: The design was a retrospective cross-sectional study. A total of 81 eyes of 81 patients were enrolled: sixty-one eyes with healthy corneas and twenty patients with subclinical keratoconus (SCKC): This initial stage includes patients with the following conditions: (1) minor topographic signs of keratoconus and suspicious topographic findings (mild asymmetric bow tie, with or without deviation; (2) average K (mean corneal curvature) <46, 5 D; (3) minimum corneal thickness (ECM) > 490 μm; (4) no slit lamp found; and (5) contralateral clinical keratoconus of the eye. Pentacam topographic and Corvis biomechanical variables were collected. Decision tree and random forest were used as machine learning techniques for classifications. Random forest performed a ranking of the most critical variables in classification. (3) Results: The essential variable was SP A1 (stiffness parameter A1), followed by A2 time, posterior coma 0º, A2 velocity and peak distance. The model efficiently predicted all patients with subclinical keratoconus (Sp = 93%) and was also a good model for classifying healthy cases (Sen = 86%). The overall accuracy rate of the model was 89%. (4) Conclusions: The random forest model was a good model for classifying subclinical keratoconus. The SP A1 variable was the most critical determinant in classifying and identifying subclinical keratoconus, followed by A2 time.

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Section: Discussionmentioning

confidence: 99%

Diagnosis of Subclinical Keratoconus Based on Machine Learning Techniques

Castro-Luna

Jiménez-Rodríguez

Castaño-Fernández

et al. 2021

JCM

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“…The built system allowed keratoconus identification with a specificity of 94.1% and a sensitivity of 97.7%. In [ 16 ], authors have developed a BNN-based system of keratoconus classification. Classification accuracy of this system, using 16 parameters on a dataset of 60 elements, was 100%.…”

Section: Related Workmentioning

confidence: 99%

Keratoconus Severity Classification Using Features Selection and Machine Learning Algorithms

Aatila

Lachgar

Hrimech

et al. 2021

Computational and Mathematical Methods in Medicine

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Keratoconus is a noninflammatory disease characterized by thinning and bulging of the cornea, generally appearing during adolescence and slowly progressing, causing vision impairment. However, the detection of keratoconus remains difficult in the early stages of the disease because the patient does not feel any pain. Therefore, the development of a method for detecting this disease based on machine and deep learning methods is necessary for early detection in order to provide the appropriate treatment as early as possible to patients. Thus, the objective of this work is to determine the most relevant parameters with respect to the different classifiers used for keratoconus classification based on the keratoconus dataset of Harvard Dataverse. A total of 446 parameters are analyzed out of 3162 observations by 11 different feature selection algorithms. Obtained results showed that sequential forward selection (SFS) method provided a subset of 10 most relevant variables, thus, generating the highest classification performance by the application of random forest (RF) classifier, with an accuracy of 98% and 95% considering 2 and 4 keratoconus classes, respectively. Found classification accuracy applying RF classifier on the selected variables using SFS method achieves the accuracy obtained using all features of the original dataset.

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“…If fitness values are equal then each candidate is assigned with probability 1 represented by Eq. (9).…”

Section: Cat Swarm Optimization (Cso)mentioning

confidence: 99%

“…A simple Bayesian network classifier is presented to evaluate normal and keratoconus eyes samples for keratoconus identification. This utilizes previously developed topographic indices computed directly from the digital analysis of the Placido ring images [9].…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of Keratoconus with Corneal Features Obtained through LBP, LDP, LOOP and CSO

Subramanian

Ramesh

2021

Trends Sci

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Keratoconus, by its name, is the condition of the eye wherein the cornea assumes a conical shape due to the thinning and protrusion of the cornea. Keratoconus, though bilateral, can be asymmetric in that it can progress differently in the eyes of the patient. Keratoconus can start from early adulthood and progress till the age of 40. Early detection of keratoconus is vital in preventing vision loss or costly repairs. The diagnostic tools available range from keratoscope to videokeratoscope but involves human efforts and thereby human errors. Automatic detection of keratoconus is required for large screening camps. With the advances in artificial intelligence techniques for medical diagnosis, new algorithms and techniques have been developed for the early and rapid screening of keratoconus, which aids clinicians in fast diagnosis. Artificial Neural Networks, Support Vector Machines, Radial Basis Function Neural Networks, Decision Trees, Computational Neural Networks, and various optimisation techniques have been used in different studies. The progression of keratoconus is identified by analyzing the shape of the cornea with Local Binary Pattern (LBP), Local Directional Pattern (LDP), Local Optimal Oriented Pattern (LOOP), and Cat Swarm Optimization (CSO) to detect the changes in cornea edges. The image processing with the CSO algorithm optimizes the result for the changes in the cornea and keratoconus detection. A new automated solution for detecting keratoconus is presented that employs texture analysis techniques such as LBP, LDP, LOOP, and CSO. The CSO extracts morphological and granular features from images of the cornea. The proposed method can be used to detect keratoconus by identifying the cornea shape change and improving clinical decisions. Further research can be in the way of grading the level of keratoconus. HIGHLIGHTS Extraction of corneal features for Diagnosis of Keratoconus from corneal topography images Feature vector extraction from corneal images using Local Binary Pattern, Local Direction Pattern and Local Optimal Oriented Pattern Optimisation using Cat Swarm Optimisation GRAPHICAL ABSTRACT

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Robust keratoconus detection with Bayesian network classifier for Placido-based corneal indices

Cited by 19 publications

References 33 publications

Diagnosis of Subclinical Keratoconus Based on Machine Learning Techniques

Diagnosis of Subclinical Keratoconus Based on Machine Learning Techniques

Keratoconus Severity Classification Using Features Selection and Machine Learning Algorithms

Diagnosis of Keratoconus with Corneal Features Obtained through LBP, LDP, LOOP and CSO

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