2019
DOI: 10.1016/j.eswa.2019.112821
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A systematic survey of computer-aided diagnosis in medicine: Past and present developments

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Cited by 228 publications
(113 citation statements)
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References 169 publications
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“…Artificial neural network (ANN): Computer-aided diagnosis has advanced due to the application of machine-learning techniques [27]. In particular, our own previous work [28][29][30], as well as numerous works in related literature [31] have demonstrated the effectiveness and modeling flexibility of ANNs for computer-aided diagnosis in medicine.…”
Section: Classification Methodsmentioning
confidence: 99%
“…Artificial neural network (ANN): Computer-aided diagnosis has advanced due to the application of machine-learning techniques [27]. In particular, our own previous work [28][29][30], as well as numerous works in related literature [31] have demonstrated the effectiveness and modeling flexibility of ANNs for computer-aided diagnosis in medicine.…”
Section: Classification Methodsmentioning
confidence: 99%
“…A recent development in this history relates to the fact that, due to the generalized expansion of HISs, increasing volumes of data about patients are being recorded, flooding physicians under data (Pivovarov and Elhadad, 2015). This large amount of data quickly proved to be too difficult to analyze by human brains (Yanase and Triantaphyllou, 2019). Data mining and machine learning algorithms are better suited to this task, thanks to their distinctive efficiency when it comes to treating large amounts of data, unveiling correlations, approximating risk functions, or solving classification problems.…”
Section: A Loss Of Confidence In Physicians' Diagnostic Skills and Knmentioning
confidence: 99%
“…These systems will be our main topic in the present article. According to a recent systematic survey of DDSSs (Yanase and Triantaphyllou, 2019), there are currently two main types of DDSSs:…”
Section: Introductionmentioning
confidence: 99%
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“…They also process and apply filters to these signals to remove unwanted noise. Ye Yuan, ext al, 2017 [24]: proposed a multi-context learning approach for EEG epileptic seizure detection .They generated EEG sequences from EEG records by using wave transduction to describe the frequency content over time. The experimental results indicate that the representative context features can be obtained from the suggested model from a number of viewpoints and thus enhance the efficiency of EEG seizure detection.…”
Section: Related Workmentioning
confidence: 99%