Multichannel heuristic learning based single layer neural network filter for mixed noise suppression from color Doppler ultrasound images

Kumar, Manish; Mishra, Sudhansu Kumar; Choubey, Dilip Kumar; Jangir, Sunil Kumar; Goyal, Dinesh

doi:10.1007/s11554-020-01061-z

Cited by 2 publications

(2 citation statements)

References 26 publications

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“…This network is a derived from Convolutional Neural Network (CNN) and Functional Link Artificial Neural Network (FLANN). In which FLANN can solve any nonlinear problem and no hidden layer present in the architecture of it 29,30 . Similarly, in CNN has a series of hidden layer present in the last layer that is, fully connected feed‐forward side 31 .…”

Section: Introductionmentioning

confidence: 99%

“…In which FLANN can solve any nonlinear problem and no hidden layer present in the architecture of it. 29,30 Similarly, in CNN has a series of hidden layer present in the last layer that is, fully connected feed-forward side. 31 It has capability of extract feature through filter layers and can provide efficient solution of nonlinear engineering problems like diagnosis, recognition, classification and prediction.…”

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confidence: 99%

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Functional link convolutional neural network for the classification of diabetes mellitus

Jangir

Joshi

Kumar

et al. 2021

Numer Methods Biomed Eng

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Diabetes is a faction of metabolic ailments distinguished by hyperglycemia which is the consequence of a defect, in the action of insulin, insulin secretion, or both and producing various abnormalities in the human body. In recent years, the utilization of intelligent systems has been expanded in disease classification and numerous researches have been proposed. In this research article, a variant of Convolutional Neural Network (CNN) that is, Functional Link Convolutional Neural Network (FLCNN) is proposed for the diabetes classification. The main goal of this article is to find the potential of a computationally less complex deep learning network like FLCNN and applied the proposed technique on a real dataset of diabetes for classification. This article also presents the comparative studies where various other machine learning techniques are implemented and outcomes are compared with the proposed FLCNN network. The performance of each classification techniques have been evaluated based on standard measures and also validated with a non‐parametric statistical test such as Friedman. Data for modeling diabetes classification is collected from Bombay Medical Hall, Upper Bazar, Ranchi, India. Accuracy achieve by the proposed classifier is more than 90% which is closer to the other state‐of‐the‐art implemented classifiers.

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Section: Introductionmentioning

confidence: 99%

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confidence: 99%