Pre-diagnosis of Diabetic Retinopathy using Blob Detection

Dharmana, Meher Madhu; Aiswarya, M.

doi:10.1109/icirca48905.2020.9183241

Cited by 19 publications

(2 citation statements)

References 14 publications

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“…In 2020, Dharmana and Aiswaraya [18] have developed Utilizing Blob Detection, pre-diagnosis of diabetic retinal disease. The method presented in this work proposes an efficient feature extraction technique centered on blob detection, followed by machine learning methodology categorization of various phases of diabetic retinopathy.…”

Section: Literature Reviewmentioning

confidence: 99%

Deep Learning Empowered Diabetic Retinopathy Detection and Classification using Retinal Fundus Images

Kuna

Prasad²

2023

IJRITCC

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Diabetic Retinopathy (DR) is a commonly occurring disease among diabetic patients that affects retina lesions and vision. Since DR is irreversible, an earlier diagnosis of DR can considerably decrease the risk of vision loss. Manual detection and classification of DR from retinal fundus images is time-consuming, expensive, and prone to errors, contrasting to CAD models. In recent times, DL models have become a familiar topic in several applications, particularly medical image classification. With this motivation, this paper presents new deep learning-empowered diabetic retinopathy detection and classification (DL-DRDC) model. The DL-DRDC technique aims to recognize and categorize different grades of DR using retinal fundus images. The proposed model involves the Contrast Limited Adaptive Histogram Equalization (CLAHE) technique as a pre-processing stage, which is used to enhance the contrast of the fundus images and improve the low contrast of medical images. Besides, the CLAHE is applied to the L channel of the retina images that have higher contrast. In addition, a deep learning-based Efficient Net-based feature extractor is used to generate feature vectors from pre-processed images. Moreover, a deep neural network (DNN) is used as a classifier model to allocate proper DR stages. An extensive set of experimental analyses takes place using a benchmark MESSIDOR dataset and the results are examined interms of different evaluation parameters. The simulation values highlighted the better DR diagnostic efficiency of the DL-DRDC technique over the recent techniques.

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Section: Literature Reviewmentioning

confidence: 99%

Deep Learning Empowered Diabetic Retinopathy Detection and Classification using Retinal Fundus Images

Kuna

Prasad²

2023

IJRITCC

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“…Automatic localization of the regions of a RI might include a certain number of lesions that can significantly help the medical experts in DR detection. An investigation of the Deep Learning (DL)-Artificial Intelligence (AI) method was presented in [4] for DR treatment and prognosis. A novel automatic detection of DR and maculopathy in eye fundus images utilizing fuzzy image processing was designed.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning Based Diagnosis for Diabetic Retinopathy for SKPD-PSC

Thiruvenkatasuresh¹,

Bhatia²,

Basheer³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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The study aimed to apply to Machine Learning (ML) researchers working in image processing and biomedical analysis who play an extensive role in comprehending and performing on complex medical data, eventually improving patient care. Developing a novel ML algorithm specific to Diabetic Retinopathy (DR) is a challenge and need of the hour. Biomedical images include several challenges, including relevant feature selection, class variations, and robust classification. Although the current research in DR has yielded favourable results, several research issues need to be explored. There is a requirement to look at novel pre-processing methods to discard irrelevant features, balance the obtained relevant features, and obtain a robust classification. This is performed using the Steerable Kernalized Partial Derivative and Platt Scale Classifier (SKPD-PSC) method. The novelty of this method relies on the appropriate non-linear classification of exclusive image processing models in harmony with the Platt Scale Classifier (PSC) to improve the accuracy of DR detection. First, a Steerable Filter Kernel Pre-processing (SFKP) model is applied to the Retinal Images (RI) to remove irrelevant and redundant features and extract more meaningful pathological features through Directional Derivatives of Gaussians (DDG). Next, the Partial Derivative Image Localization (PDIL) model is applied to the extracted features to localize candidate features and suppress the background noise. Finally, a Platt Scale Classifier (PSC) is applied to the localized features for robust classification. For the experiments, we used the publicly available DR detection database provided by Standard Diabetic Retinopathy (SDR), called DIARETDB0. A database of 130 image samples has been collected to train and test the ML-based classifiers. Experimental results show that the proposed method that combines the image processing and ML models can attain good detection performance with a high DR detection accuracy rate with minimum time and complexity compared to the state-of-the-art methods. The accuracy and speed of DR detection for numerous types of images will be tested through experimental evaluation. Compared to state-of-the-art methods, the method increases DR detection accuracy by 24% and DR detection time by 37.

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