Finite-interval-valued Type-2 Gaussian fuzzy numbers applied to fuzzy TODIM in a healthcare problem

Tolga, A. Çağrı; Parlak, İsmail Burak; Castillo, Oscar

doi:10.1016/j.engappai.2019.103352

Cited by 104 publications

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References 36 publications

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“…Li et al (2018) used Den-seNet-121 and DenseNet-RNN were two deep learning models utilized to analyze the infections in ChestX-Ray14, where DenseNet-121 getting a sum of 74.5% and DenseNet-RNN was 75.1% to recognizing Pneumonia. Rajpurkar et al (2017) were introduced by taking 121 layers to identify one of the 14 infections at 76.8% of accuracy of the pneumonic class from the others; likewise this model gives a heatmap for possible localization that depends on the forecast done by the convolutional neural network, and more study can be found by applying machine learning and deep learning algorithm to analyze the X-ray and CT images (Basu et al 2020;Pavithra et al 2015;Ozkaya et al 2020;Santos and Melin 2020;Tolga et al 2020;Ramírez et al 2019;Miramontes et al 2018;Melin et al 2018;Kermany, et al 2018b, a;Ayan, and Ü nver, 2019;Varshni et al 2019;Wang et al 2017;Togaçar et al 2019;Jaiswal, et al 2019;Sirazitdinov, et al 2019;Behzadi-khormouji et al 2020;Stephen et al 2019;Xu et al 2020;Shan et al 2020).…”

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confidence: 99%

Early diagnosis of COVID-19-affected patients based on X-ray and computed tomography images using deep learning algorithm

et al. 2020

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The novel coronavirus infection (COVID-19) that was first identified in China in December 2019 has spread across the globe rapidly infecting over ten million people. The World Health Organization (WHO) declared it as a pandemic on March 11, 2020. What makes it even more critical is the lack of vaccines available to control the disease, although many pharmaceutical companies and research institutions all over the world are working toward developing effective solutions to battle this life-threatening disease. X-ray and computed tomography (CT) images scanning is one of the most encouraging exploration zones; it can help in finding and providing early diagnosis to diseases and gives both quick and precise outcomes. In this study, convolution neural networks method is used for binary classification pneumonia-based conversion of VGG-19, Inception_V2 and decision tree model on X-ray and CT scan images dataset, which contains 360 images. It can infer that fine-tuned version VGG-19, Inception_V2 and decision tree model show highly satisfactory performance with a rate of increase in training and validation accuracy (91%) other than Inception_V2 (78%) and decision tree (60%) models. Keywords COVID-19 Á X-ray images Á CT scan Á CNN Á VGG-16 Á Inception_V2 Á Decision tree 1 Introduction The first case of the virus became exposed in Wuhan city of China in November 2019, there are 1,100,000 peoples living in this city and it interfaces numerous urban communities of China. The outbreak of atypical and individual-to-individual transmissible pneumonia brought about by the severe acute respiratory syndrome coronavirus-2 (SARS-COV-2) has caused a worldwide. There have been in excess of 26,000,000 confirmed cases of the corona virus disease (COVID-19) on the globe, as of April 23, 2020. As indicated by the WHO, 16-21% of individuals with the infection have gotten seriously sick with a Communicated by Valentina E. Balas.

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Early diagnosis of COVID-19-affected patients based on X-ray and computed tomography images using deep learning algorithm

et al. 2020

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“…Also, we will define some more generalized algorithms to solve more complex problems such as brain hemorrhage, healthcare, nonlinear systems, control systems, and others. [47][48][49][50][51][52][53]…”

Section: Comparative Studiesmentioning

confidence: 99%

A new possibility degree measure for interval‐valued q‐rung orthopair fuzzy sets in decision‐making

Garg

2020

Int J Intell Syst

107

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“…Gribova and Shalfeeva 11 summarized their experiences in building intelligent systems for the diagnosis of processes, and presented a universal ontology of anomalous processes diagnosis. For more relevant works, readers can refer to Tolga et al, 12 Miramontes et al, 13 and Ghasemi et al 14 The other is mainly to use AI methods for medical images identification and classification. Zhang et al 15 proposed an AI‐driven pathology whole‐slide diagnosis method that matched the performance of 17 pathologists in the diagnosis of urothelial carcinoma.…”

Section: Introductionmentioning

confidence: 99%

A machine learning‐based risk scoring system for infertility considering different age groups

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Finite-interval-valued Type-2 Gaussian fuzzy numbers applied to fuzzy TODIM in a healthcare problem

Cited by 104 publications

References 36 publications

Early diagnosis of COVID-19-affected patients based on X-ray and computed tomography images using deep learning algorithm

Early diagnosis of COVID-19-affected patients based on X-ray and computed tomography images using deep learning algorithm

A new possibility degree measure for interval‐valued q‐rung orthopair fuzzy sets in decision‐making

A machine learning‐based risk scoring system for infertility considering different age groups

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