Automated detection for Retinopathy of Prematurity with knowledge distilling from multi-stream fusion network

Shen, Yingshan; Luo, Zirong; Xu, Muxin; Liang, Zhihao; Fan, Xiaomao; Lu, Xiaoyan

doi:10.1016/j.knosys.2023.110461

Cited by 3 publications

(1 citation statement)

References 48 publications

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“…The massive potential of various types of ANN models in the healthcare domain must be addressed. Recent literature [16,17] has extensively studied the potential of ANN for the treatment and diagnosis of a wide range of infectious diseases, such as diarrhea [18], tuberculosis [19], dengue [20], COVID-19 [21,22], and childhood blindness [23]. State-ofthe-art schemes generally considered accuracy and complexity as the primary performance metrics to examine the performance of ANN.…”

Section: Introductionmentioning

confidence: 99%

Neural Networks for the Detection of COVID-19 and Other Diseases: Prospects and Challenges

et al. 2023

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Artificial neural networks (ANNs) ability to learn, correct errors, and transform a large amount of raw data into beneficial medical decisions for treatment and care has increased in popularity for enhanced patient safety and quality of care. Therefore, this paper reviews the critical role of ANNs in providing valuable insights for patients’ healthcare decisions and efficient disease diagnosis. We study different types of ANNs in the existing literature that advance ANNs’ adaptation for complex applications. Specifically, we investigate ANNs’ advances for predicting viral, cancer, skin, and COVID-19 diseases. Furthermore, we propose a deep convolutional neural network (CNN) model called ConXNet, based on chest radiography images, to improve the detection accuracy of COVID-19 disease. ConXNet is trained and tested using a chest radiography image dataset obtained from Kaggle, achieving more than 97% accuracy and 98% precision, which is better than other existing state-of-the-art models, such as DeTraC, U-Net, COVID MTNet, and COVID-Net, having 93.1%, 94.10%, 84.76%, and 90% accuracy and 94%, 95%, 85%, and 92% precision, respectively. The results show that the ConXNet model performed significantly well for a relatively large dataset compared with the aforementioned models. Moreover, the ConXNet model reduces the time complexity by using dropout layers and batch normalization techniques. Finally, we highlight future research directions and challenges, such as the complexity of the algorithms, insufficient available data, privacy and security, and integration of biosensing with ANNs. These research directions require considerable attention for improving the scope of ANNs for medical diagnostic and treatment applications.

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Section: Introductionmentioning

confidence: 99%

Neural Networks for the Detection of COVID-19 and Other Diseases: Prospects and Challenges

et al. 2023

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StAlK: Structural Alignment based Self Knowledge distillation for Medical Image Classification

Sharma,

Kumar,

Monpara

et al. 2024

Knowledge-Based Systems

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Applying novel self‐supervised learning for early detection of retinopathy of prematurity

Wang,

Qiao,

Guo

et al. 2024

Electronics Letters

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Retinopathy of prematurity (ROP) mainly occurs in premature infants with low birth weight, and it is the leading cause of childhood blindness. Early and accurate ROP diagnosis is imperative for appropriate treatment. However, less research concentrates on early‐stage ROP diagnosis based on limited‐labelled images in an imbalanced dataset. To address the dilemma, this study proposed a novel self‐supervised network, MOCO‐MIM, for early ROP grading. The proposed classification network was evaluated on a total of 553 labelled fundus images from 89 preterm infants. The trained network achieved a test accuracy of 98.29% and an AUC score of 97.6% for three stages of grading. The adopted method is verified that the proposed method can be detected early stages of ROP more efficiently and grade the severity more accurately based on limited‐labelled fundus images, which is superior to the existing state‐of‐the‐art methods.

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Automated detection for Retinopathy of Prematurity with knowledge distilling from multi-stream fusion network

Cited by 3 publications

References 48 publications

Neural Networks for the Detection of COVID-19 and Other Diseases: Prospects and Challenges

Neural Networks for the Detection of COVID-19 and Other Diseases: Prospects and Challenges

StAlK: Structural Alignment based Self Knowledge distillation for Medical Image Classification

Applying novel self‐supervised learning for early detection of retinopathy of prematurity

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