Artificial Neural Network Model in Stroke Diagnosis

Mirtskhulava, Lela; Wong, Julian; Al-Majeed, Salah; Pearce, Gillian

doi:10.1109/uksim.2015.33

Cited by 16 publications

(10 citation statements)

References 6 publications

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“…Here, this technique is described via following some examples. Neural network was used in stroke diagnosis [6], where the input parameters were given as Xi 1 , …, X ip and p = 16 stroke-related symptoms, together with acute confusion, problem of vision and mobility, paresthesia of the leg or arm, etc. Y i represents the binary outcome, where Y i = 1/0 represents that the i th patient has or does not have stroke.…”

Section: Neural Networkmentioning

confidence: 99%

Application of Artificial Intelligence in Modern Healthcare System

Datta¹,

Barua²,

Das³

2020

Alginates - Recent Uses of This Natural Polymer

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Artificial intelligence (AI) has the potential of detecting significant interactions in a dataset and also it is widely used in several clinical conditions to expect the results, treat, and diagnose. Artificial intelligence (AI) is being used or trialed for a variety of healthcare and research purposes, including detection of disease, management of chronic conditions, delivery of health services, and drug discovery. In this chapter, we will discuss the application of artificial intelligence (AI) in modern healthcare system and the challenges of this system in detail. Different types of artificial intelligence devices are described in this chapter with the help of working mechanism discussion. Alginate, a naturally available polymer found in the cell wall of the brown algae, is used in tissue engineering because of its biocompatibility, low cost, and easy gelation. It is composed of α-L-guluronic and β-D-manuronic acid. To improve the cell-material interaction and erratic degradation, alginate is blended with other polymers. Here, we discuss the relationship of artificial intelligence with alginate in tissue engineering fields.

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Section: Neural Networkmentioning

confidence: 99%

Application of Artificial Intelligence in Modern Healthcare System

Datta¹,

Barua²,

Das³

2020

Alginates - Recent Uses of This Natural Polymer

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“…Another version of inputs is that in which they consider symptoms along with numerical inputs as proposed by Mirtskhulava et. al [14]. They modelled a neural network model having two h idden layers wh ich accepted 16 inputs to predict the risk of stroke in the examined patient.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Neuro-Fuzzy Inference System for Prediction of Surgery Time for Ischemic Stroke Patients

Ali

Qidwai

Ilyas

et al. 2019

IJIE

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There is a worldwide trend in the increase of the number of stroke cases reported every year. According to the World Health Organizat ion, stroke is one of the leading causes of death in 2016 with appro ximately 5.8 million deaths reported in 2016 alone. While a stroke could happen to anyone, mu ltiple risk factors such as age, hypertension, obesity and diabetes contribute significantly to its occurrence.

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“…In our method, the SVM classification technique is utilized for the classification process. The sensitivity and accuracy of our method are compared with the existing Decision tree classifier [29], Artificial Neural Network [18] and K-Nearest Neighbor (K-NN) [30].…”

Section: Quantitative Analysismentioning

confidence: 99%

“…In our method, the SVM classification technique is utilized for the classification process. The RMSE, MBE and MAPE of our method are compared with the existing ANN [18], ANN-FFA [19] and RF-FFA [25]. Compared with the existing method, our proposed method has 17.64% RMSE, 6.24% MAPE and 2.55% MBE and the consumption time is 6.45 (s).…”

Section: Performance Evaluationmentioning

confidence: 99%

See 1 more Smart Citation

A Crossbred Approach for Effective Brain Stroke Lesion Segmentation

Melingi¹,

Vivekanand²

2018

IJIES

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An Ischemic stroke is expressed as lost neurological brain work because of the sudden loss of blood dissemination in the specific territory of the brain. The sub-acute ischemic stroke is the most basic illnesses reason for death on the planet. In this paper we utilize a hybrid way to deal with detecting the ischemic stroke from the alternate pathologies in magnetic resonance (MR) images utilizing Kernelized Fuzzy C-means (KFCM) clustering with adaptive threshold algorithm and the Support Vector Machine (SVM) classifier. In the existing method, the Otsu's method incorporated with SVM classifier method is utilized for the segmentation of the ischemic stroke image, but it has the limited accuracy 88%, specificity 66% and the sensitivity value is 94%. For the exact identification and segmentation the KFCM algorithm is utilized. The distance and the intensity of the lesion tissue is identified by this method. The accuracy and segmentation aftereffects of the classifier are measured in the testing and training phase by looking at the comparable and a decent variety of sample sets by considering diverse groupings. Our test comes about demonstrating that, the performance of the proposed technique is assessed in view of the precision, recall, sensitivity, accuracy and overlap metrics of the framework. Compared with the existing classification method, the proposed method has 17.64% RMSE (Root Mean Square Error), 6.24% MAPE (Mean Absolute Percentage Error) and 2.55% MBE (Mean Bias Error), consumption time is 6.45 (s) and also the sensitivity and the accuracy ranges are 98.8% and 99%. The proposed approach is actualized using MATLAB and the realtime datasets are used for our examination.

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Artificial Neural Network Model in Stroke Diagnosis

Abstract: The University of Gloucestershire accepts no liability for any infringement of intellectual property rights in any material deposited but will remove such material from public view pending investigation in the event of an allegation of any such infringement.

Cited by 16 publications

References 6 publications

Application of Artificial Intelligence in Modern Healthcare System

Application of Artificial Intelligence in Modern Healthcare System

Adaptive Neuro-Fuzzy Inference System for Prediction of Surgery Time for Ischemic Stroke Patients

A Crossbred Approach for Effective Brain Stroke Lesion Segmentation

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