Computer-aided diabetic retinopathy diagnostic model using optimal thresholding merged with neural network

Jadhav, Ambaji S.; Patil, Pushpa B.; Biradar, Sunil G

doi:10.1108/ijicc-11-2019-0119

Cited by 7 publications

(4 citation statements)

References 36 publications

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“…Articles with 1 entry each: (Gharaibeh and Alshorman ( 2016 ) Raju et al ( 2017 ) Ting et al ( 2017 ) Abbas et al ( 2017 ) Mansour ( 2018 ) Brown et al ( 2018 ) Randive et al ( 2018 ) Ramachandran et al ( 2018 ) Hemanth et al ( 2018 ) ( 2019 ) Saleh et al ( 2018 ) Gao et al ( 2018 ) Gonzalez-Gonzalo et al ( 2020 ) Sahlsten et al ( 2019 ) Liu et al ( 2019 ) Sun ( 2019 ) Zhang et al ( 2019 ) Pires et al ( 2019 ) Qummar et al ( 2019 ) Zeng et al ( 2019 ) Nazir et al ( 2019 ) Li et al ( 2019b ) Wu et al ( 2020 ) Shaban et al ( 2020 ) Pao et al ( 2020 ) Torre et al ( 2020 ) Shah et al ( 2020 ) Zago et al ( 2020 ) Srivastava and Purwar ( 2020 ) Shankar et al ( 2020a ) ( b ) Samanta et al ( 2020 ) Xie et al ( 2020 ) Ayhan et al ( 2020 ) Ali et al ( 2020 ) Jadhav et al ( 2020 ) Luo et al ( 2020 ) Gayathri et al ( 2020 ) Bhardwaj et al ( 2021 )…”

Section: The Study Of the New Fundus Algorithms By Their Overall Mode...mentioning

confidence: 99%

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Developments in the detection of diabetic retinopathy: a state-of-the-art review of computer-aided diagnosis and machine learning methods

et al. 2022

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The exponential increase in the number of diabetics around the world has led to an equally large increase in the number of diabetic retinopathy (DR) cases which is one of the major complications caused by diabetes. Left unattended, DR worsens the vision and would lead to partial or complete blindness. As the number of diabetics continue to increase exponentially in the coming years, the number of qualified ophthalmologists need to increase in tandem in order to meet the demand for screening of the growing number of diabetic patients. This makes it pertinent to develop ways to automate the detection process of DR. A computer aided diagnosis system has the potential to significantly reduce the burden currently placed on the ophthalmologists. Hence, this review paper is presented with the aim of summarizing, classifying, and analyzing all the recent development on automated DR detection using fundus images from 2015 up to this date. Such work offers an unprecedentedly thorough review of all the recent works on DR, which will potentially increase the understanding of all the recent studies on automated DR detection, particularly on those that deploys machine learning algorithms. Firstly, in this paper, a comprehensive state-of-the-art review of the methods that have been introduced in the detection of DR is presented, with a focus on machine learning models such as convolutional neural networks (CNN) and artificial neural networks (ANN) and various hybrid models. Each AI will then be classified according to its type (e.g. CNN, ANN, SVM), its specific task(s) in performing DR detection. In particular, the models that deploy CNN will be further analyzed and classified according to some important properties of the respective CNN architectures of each model. A total of 150 research articles related to the aforementioned areas that were published in the recent 5 years have been utilized in this review to provide a comprehensive overview of the latest developments in the detection of DR. Supplementary Information The online version contains supplementary material available at 10.1007/s10462-022-10185-6.

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Section: The Study Of the New Fundus Algorithms By Their Overall Mode...mentioning

confidence: 99%

“…Moreover, Jadhav et al ( 2020 ) devised their unique method of general DR grading. In such grading, the blood vessels are first separated by a grey level threshold, leaving the other features to have their entropy (e.g.…”

Section: The Study Of the New Fundus Algorithms By Their Overall Mode...mentioning

confidence: 99%

Developments in the detection of diabetic retinopathy: a state-of-the-art review of computer-aided diagnosis and machine learning methods

et al. 2022

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“…Connectionist temporal classification (CTC) is an algorithm that trains a deep neural network [20] for the endto-end learning task. It can make the sequence label predictions at any point in the input sequence [18].…”

Section: Baseline Modelmentioning

confidence: 99%

Multitask Learning with Local Attention for Tibetan Speech Recognition

et al. 2020

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In this paper, we propose to incorporate the local attention in WaveNet-CTC to improve the performance of Tibetan speech recognition in multitask learning. With an increase in task number, such as simultaneous Tibetan speech content recognition, dialect identification, and speaker recognition, the accuracy rate of a single WaveNet-CTC decreases on speech recognition. Inspired by the attention mechanism, we introduce the local attention to automatically tune the weights of feature frames in a window and pay different attention on context information for multitask learning. The experimental results show that our method improves the accuracies of speech recognition for all Tibetan dialects in three-task learning, compared with the baseline model. Furthermore, our method significantly improves the accuracy for low-resource dialect by 5.11% against the specific-dialect model.

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“…While traditional machine learning algorithms can reduce manual labor for steel surface identification, their effectiveness is limited by the need for manually designed features. This requires complex feature engineering operations (Jadhav et al , 2020), which can raise costs. Additionally, the detection results produced by these algorithms may still have significant shortcomings.…”

Section: Introductionmentioning

confidence: 99%

Steel surface defect classification approach using an All-optical Neuron-based SNN with attention mechanism

Gong

Dong

Cheng

et al. 2023

IJICC

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PurposeThe purpose of this study is to propose a new method for the end-to-end classification of steel surface defects.Design/methodology/approachThis study proposes an AM-AoN-SNN algorithm, which combines an attention mechanism (AM) with an All-optical Neuron-based spiking neural network (AoN-SNN). The AM enhances network learning and extracts defective features, while the AoN-SNN predicts both the labels of the defects and the final labels of the images. Compared to the conventional Leaky-Integrated and Fire SNN, the AoN-SNN has improved the activation of neurons.FindingsThe experimental findings on Northeast University (NEU)-CLS demonstrate that the proposed neural network detection approach outperforms other methods. Furthermore, the network’s effectiveness was tested, and the results indicate that the proposed method can achieve high detection accuracy and strong anti-interference capabilities while maintaining a basic structure.Originality/valueThis study introduces a novel approach to classifying steel surface defects using a combination of a shallow AoN-SNN and a hybrid AM with different network architectures. The proposed method is the first study of SNN networks applied to this task.

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Computer-aided diabetic retinopathy diagnostic model using optimal thresholding merged with neural network

Cited by 7 publications

References 36 publications

Developments in the detection of diabetic retinopathy: a state-of-the-art review of computer-aided diagnosis and machine learning methods

Developments in the detection of diabetic retinopathy: a state-of-the-art review of computer-aided diagnosis and machine learning methods

Multitask Learning with Local Attention for Tibetan Speech Recognition

Steel surface defect classification approach using an All-optical Neuron-based SNN with attention mechanism

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