Retinal Image Classification by Self-supervised Fuzzy Clustering Network

Luo, Yuling; Pan, Jing; Fan, Shaoshuai; Du, Zeyu; Zhang, Guanghua

doi:10.1109/access.2020.2994047

Cited by 31 publications

(18 citation statements)

References 36 publications

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“…Luo et al [56] introduced a Self-Supervised Fuzzy Clustering Network (SFCN) that is represented by three main modules: a feature learning module from unlabelled retinal fundus images, a fuzzy clustering module for self-supervision and a reconstruction module. Initially, convolutional layers for feature representation extraction make up the feature learning module given an input fundus image followed by deconvolutional layers for the reconstruction of the retinal images.…”

Section: ) Binary Classificationmentioning

confidence: 99%

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Deep Learning Techniques for Diabetic Retinopathy Classification: A Survey

2022

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Diabetic Retinopathy (DR) is a degenerative disease that impacts the eyes and is a consequence of Diabetes mellitus, where high blood glucose levels induce lesions on the eye retina. Diabetic Retinopathy is regarded as the leading cause of blindness for diabetic patients, especially the working-age population in developing nations. Treatment involves sustaining the patient's current grade of vision since the disease is irreversible. Early detection of Diabetic Retinopathy is crucial in order to sustain the patient's vision effectively. The main issue involved with DR detection is that the manual diagnosis process is very time, money and effort consuming, and involves an ophthalmologist's examination of eye retinal fundus images. The latter also proves to be more difficult, particularly in the early stages of the disease when disease features are less prominent in the images. Machine learning-based medical image analysis has proven competency in assessing retinal fundus images and the utilization of deep learning algorithms has aided the early diagnosis of Diabetic Retinopathy (DR). This paper reviews and analyzes state-of-the-art deep learning methods in supervised, self-supervised, Vision Transformer etc. setups, proposing retinal fundus image classification and detection. For instance, referable, non-referable, and proliferative classification of Diabetic Retinopathy are reviewed and summarized. Moreover, the paper discusses available retinal fundus datasets for Diabetic Retinopathy that are used for tasks such as detection, classification and segmentation. The paper also assesses research gaps in the area of DR detection/classification and addresses various challenges that need further study and investigation.

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Section: ) Binary Classificationmentioning

confidence: 99%

“…Luo et al [56] uses the SFCN model trained with 25 DRIVE images. It was able to achieve an accuracy of 81.7%.…”

Section: ) Binary Classificationmentioning

confidence: 99%

Deep Learning Techniques for Diabetic Retinopathy Classification: A Survey

2022

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“…The system was tested on 45 patients and achieved 100% accuracy in predicting lung cancer. Luo et al [ 41 ] developed a self-supervised model that employed fuzzy clustering. Three modules were developed: feature learning, reconstruction, and fuzzy self-supervision.…”

Section: Related Workmentioning

confidence: 99%

A Fuzzy Rule-Based System for Classification of Diabetes

Aamir

Sarfraz

Ramzan

et al. 2021

Sensors

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Diabetes is a fatal disease that currently has no treatment. However, early diagnosis of diabetes aids patients to start timely treatment and thus reduces or eliminates the risk of severe complications. The prevalence of diabetes has been rising rapidly worldwide. Several methods have been introduced to diagnose diabetes at an early stage, however, most of these methods lack interpretability, due to which the diagnostic process cannot be explained. In this paper, fuzzy logic has been employed to develop an interpretable model and to perform an early diagnosis of diabetes. Fuzzy logic has been combined with the cosine amplitude method, and two fuzzy classifiers have been constructed. Afterward, fuzzy rules have been designed based on these classifiers. Lastly, a publicly available diabetes dataset has been used to evaluate the performance of the proposed fuzzy rule-based model. The results show that the proposed model outperforms existing techniques by achieving an accuracy of 96.47%. The proposed model has demonstrated great prediction accuracy, suggesting that it can be utilized in the healthcare sector for the accurate diagnose of diabetes.

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“…Their methods were tested on two 3D down-stream tasks which are brain tumor segmentation and pancreas tumor segmentation. Luo et al [2020] proposed self-supervised fuzzy clustering network as a pretext task for color fundus photo classification.…”

Section: Sriram Et Almentioning

confidence: 99%

Self-supervised learning methods and applications in medical imaging analysis: A survey

Shurrab¹,

Duwairi²

2021

Preprint

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The availability of high quality annotated medical imaging datasets is a major problem that collides with machine learning applications in the field of medical imaging analysis and impedes its advancement. Self-supervised learning is a recent training paradigm that enables learning robust representations without the need for human annotation which can be considered as an effective solution for the scarcity in annotated medical data. This article reviews the state-of-the-art research directions in self-supervised learning approaches for image data with concentration on their applications in the field of medical imaging analysis. The article covers a set of the most recent self-supervised learning methods from the computer vision field as they are applicable to the medical imaging analysis and categorize them as predictive, generative and contrastive approaches. Moreover, the article covers (40) of the most recent researches in the field of self-supervised learning in medical imaging analysis aiming at shedding the light on the recent innovation in the field. Ultimately, the article concludes with possible future research directions in the field.

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Retinal Image Classification by Self-supervised Fuzzy Clustering Network

Cited by 31 publications

References 36 publications

Deep Learning Techniques for Diabetic Retinopathy Classification: A Survey

Deep Learning Techniques for Diabetic Retinopathy Classification: A Survey

A Fuzzy Rule-Based System for Classification of Diabetes

Self-supervised learning methods and applications in medical imaging analysis: A survey

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