Deep learning based conventional neural network architecture for medical image classification

Neelapu, Ramesh; Devi, G. Lavanya; Rao, Kurapati Srinivasa

doi:10.3166/ts.35.169-182

Cited by 35 publications

(18 citation statements)

References 28 publications

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“…Previous studies have focused in depth and broadly on feature extraction-based diabetic retinopathy diagnosis mechanisms. Then the rapid development of deep convolutional neural networks has become the focus of attention of researchers and the new technology in the classification of retinal images, and in general, in all medical image processing applications [46]. In this paper, we proposed a new early stage diabetic retinopathy diagnosis mechanism which exploits the role of deep convolutional neural networks in diagnosing diabetic retinopathy disease in retinal images.…”

Section: Discussionmentioning

confidence: 99%

A New Early Stage Diabetic Retinopathy Diagnosis Model Using Deep Convolutional Neural Networks and Principal Component Analysis

Mohammedhasan¹,

Uğuz²

2020

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Diabetic retinopathy (DR) is a disease of the retina, which leads over time to vision problems such retinal detachment, vitreous hemorrhage, glaucoma, and in worse cases leads to blindness, which can initially be controlled by periodic DR-screening. Early diagnosis will lead to greater control of the disease, whereas performing retinal examinations on all diabetic patients is an unattainable need, as diabetes is a chronic disease and its global prevalence has been steadily increasing over the past few decades. According to recent World Health Organization statistics, about 422 million people worldwide have diabetes, the majority living in low-and middle-income countries. This paper proposes a new strategy that brings the strength of convolutional neural networks (CNNs) to the diagnosis of DR. Coupled with using principal component analysis (PCA) that performs dimension reduction to improve the diagnostic accuracy, the proposed model exploiting edge-preserving guided image filtering (E-GIF) that performs as a contrast enhancement mechanism, and in addition to smoothing low gradient areas, it also accentuates strong edges. Diabetic retinopathy causes progressive damage to the blood vessels in the retina to the extent that it leaves traces and lesions in the tissues of the retina. These lesions appear in the form of edges and when processing retinal images, we seek to accentuate these edges to enable better diagnosis of diabetic retinopathy symptoms. A new CNN architecture with residual connections is used, which performs very well in diagnosing DR. The proposed model is named with RUnet-PCA: Residual U-net Deep CNN with Principal Component Analysis. The well-known AlexNet, VggNet-s, VggNet-16, VggNet-19, GoogleNet, and ResNet models were adopted for comparison with the proposed model. Publicly available Kaggle dataset was employed for training exploring the DR diagnosis accuracy. Experimental results show that the proposed RUnet-PCA model achieved a diagnosis accuracy of 98.44% and it was extremely robust and promising in comparison to other diagnosis methods.

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Section: Discussionmentioning

confidence: 99%

A New Early Stage Diabetic Retinopathy Diagnosis Model Using Deep Convolutional Neural Networks and Principal Component Analysis

Mohammedhasan¹,

Uğuz²

2020

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“…To classify our data sequence, we trained a deep neural network [86]. Thus, we created an LSTM classification network [87].…”

Section: Classificationmentioning

confidence: 99%

A Novel Spatio-Temporal Violence Classification Framework Based on Material Derivative and LSTM Neural Network

Lejmi¹,

Khalifa²,

Mahjoub³

2020

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In the current era, the implementation of automated security video surveillance systems is particularly needy in terms of human violence recognition. Nevertheless, the latter encounters various interlinked difficulties which require efficient solutions as well as feasible methods that provide a relevant distinction between normal human actions and abnormal ones. In this paper, we present an overview of these issues and a literature review of the related works and current research on-going efforts on this field and suggests a novel prediction model for violence recognition, based on a preliminary spatio-temporal features extraction using the material derivative which describes the rate of change of a particle while in motion with respect to time. The classification algorithm is then carried out using a deep learning LSTM technique to classify generated features into eight specified violent and non-violent categories and a prediction value for each class of action is calculated. The whole model is trained on a public dataset and its classification capacity is evaluated on a confusion matrix which assembles all the predictions made by the system with their actual labels. The obtained results are promising and show that the proposed model can be potentially useful for detecting human violence.

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“…The CNN is suitable for analyzing an image containing a single object [39], [40]. If the image contains multiple objects, it should be split into multiple parts.…”

Section: B Faster R-cnn -Object Recognitionmentioning

confidence: 99%

Simulation of Evacuating Crowd Based on Deep Learning and Social Force Model

Liang

Zhao

et al. 2019

IEEE Access

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It is always difficult to evacuate crowds in public places like subway stations. The traditional crowd behavior simulation models often ignore two important issues in crowd evacuation: pedestrian tracking and individual differences. To solve the problem, this paper combines social force model (SFM) with deep learning into a novel pedestrian detection method. Firstly, several deep learning algorithms for pedestrian detection were compared, and the best ones for sparse and dense crowds were determined. Next, the pedestrian positions in a real video were acquired by the selected algorithms, and converted into actual coordinates in the scene. Then, the evacuation process was simulated with our method and the SFM based on these coordinates. The results show that our model output closer-to-reality results than the SFM. The research findings shed important new light on evacuation in crowded areas. INDEX TERMS Deep learning, social force model (SFM), crowd simulation.

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Deep learning based conventional neural network architecture for medical image classification

Cited by 35 publications

References 28 publications

A New Early Stage Diabetic Retinopathy Diagnosis Model Using Deep Convolutional Neural Networks and Principal Component Analysis

A New Early Stage Diabetic Retinopathy Diagnosis Model Using Deep Convolutional Neural Networks and Principal Component Analysis

A Novel Spatio-Temporal Violence Classification Framework Based on Material Derivative and LSTM Neural Network

Simulation of Evacuating Crowd Based on Deep Learning and Social Force Model

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