WITHDRAWN: Gabor filter and machine learning based diabetic retinopathy analysis and detection

Vijayan, T.; Sangeetha, M.; Kumaravel, A.; Karthik, B.

doi:10.1016/j.micpro.2020.103353

Cited by 16 publications

(2 citation statements)

References 13 publications

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“…As indicated in recent publically available literature assessments, critical variables for diagnosing eye abnormalities include an examination of the eyes' colour, texture, and form. The Gabor Filter was used to analyse the texture [38][39] and it was compared to a independent component analysis (ICA) method [40] for determining the most acceptable features in the image dataset. ICA assists in the discovery of a reduced projection picture or sub space of the original image with decreased dimensions.…”

Section: B Optimization Based Feature Engineeringmentioning

confidence: 99%

Feature Selection Pipeline based on Hybrid Optimization Approach with Aggregated Medical Data

Kaur¹,

Singh²

2022

IJACSA

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For quite some time, the usage of many sources of data (data fusion) and the aggregation of that data have been underappreciated. For the purposes of this study, trials using several medical datasets were conducted, with the results serving as a single aggregated source for identifying eye illnesses. It is proposed in this paper that a diagnostic system that can detect diabetic retinopathy, glaucoma, and cataract can be built as an alternative to current methods. The data fusion and data aggregation techniques used to create this multi-model system made it conceivable. As the name implies, it is a way of compiling data from a large number of legitimate sources. The development of a pipeline of algorithms was accomplished through iterative trials and hyper parameter tweaking. CLAHE (Contrast Level Adaptive Histogram Equalization) approaches, which increase the gradient between picture edges, improve segmentation by raising the contrast between picture edges. The Gabor filter has been shown to be the most effective method of selecting features. The Gabor filter was selected using a hybrid optimization method (LION + Cuckoo), which was developed by the author. For automation, the Support Vector Machine (SVM) radial is the most effective method since it delivers excellent stability and accuracy in terms of accuracy and recall, as well as precision and recall. The discoveries and approaches detailed here provide a more solid foundation for future image-based diagnostics researchers to build on in the future. Eventually, the findings of this study will help to improve healthcare workflows and practices.

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Section: B Optimization Based Feature Engineeringmentioning

confidence: 99%

Feature Selection Pipeline based on Hybrid Optimization Approach with Aggregated Medical Data

Kaur¹,

Singh²

2022

IJACSA

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“…The pre-processed fundus image can provide differentiating features to diagnose and categorize HR. Building features based on statistics, color, intensity, shape, structure, and texture can be achieved by creating a master feature vector [13][14][15][16][17]. Deep learning models extract the discriminative features from the training data without manual intervention and then classify the test data according to multiple grades.…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection and Classification of Hypertensive Retinopathy with Improved Convolution Neural Network and Improved SVM

Bhimavarapu,

Chintalapudi,

Battineni

2024

Bioengineering

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Hypertensive retinopathy (HR) results from the microvascular retinal changes triggered by hypertension, which is the most common leading cause of preventable blindness worldwide. Therefore, it is necessary to develop an automated system for HR detection and evaluation using retinal images. We aimed to propose an automated approach to identify and categorize the various degrees of HR severity. A new network called the spatial convolution module (SCM) combines cross-channel and spatial information, and the convolution operations extract helpful features. The present model is evaluated using publicly accessible datasets ODIR, INSPIREVR, and VICAVR. We applied the augmentation to artificially increase the dataset of 1200 fundus images. The different HR severity levels of normal, mild, moderate, severe, and malignant are finally classified with the reduced time when compared to the existing models because in the proposed model, convolutional layers run only once on the input fundus images, which leads to a speedup and reduces the processing time in detecting the abnormalities in the vascular structure. According to the findings, the improved SVM had the highest detection and classification accuracy rate in the vessel classification with an accuracy of 98.99% and completed the task in 160.4 s. The ten-fold classification achieved the highest accuracy of 98.99%, i.e., 0.27 higher than the five-fold classification accuracy and the improved KNN classifier achieved an accuracy of 98.72%. When computation efficiency is a priority, the proposed model’s ability to quickly recognize different HR severity levels is significant.

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Optimised Back Propagation-Based Deep Residual Learning Network Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Images

Shilpa,

Karthik

2024

Algorithms for Intelligent Systems

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WITHDRAWN: Gabor filter and machine learning based diabetic retinopathy analysis and detection

Cited by 16 publications

References 13 publications

Feature Selection Pipeline based on Hybrid Optimization Approach with Aggregated Medical Data

Feature Selection Pipeline based on Hybrid Optimization Approach with Aggregated Medical Data

Automatic Detection and Classification of Hypertensive Retinopathy with Improved Convolution Neural Network and Improved SVM

Optimised Back Propagation-Based Deep Residual Learning Network Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Images

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