Classification of Echocardiogram View using A Convolutional Neural Network

The standard views in echocardiography capture distinct slices of the heart which can be used to assess cardiac function. Determining the view of a given echocardiogram is the first step for analysis. To automate this step, a deep network of the ResNet-18 architecture was used to classify between six standard views. The network parameters were pre-trained with the ImageNet database and prediction quality was assessed with a visualization tool known as gradient-weighted class activation mapping (Grad-CAM). The network was able to distinguish between three parasternal short axis views and three apical views to ~99\% accuracy. 10-fold cross validation showed a 97\%-98\% accuracy for the apical view subcategories (which included apical two-, three-, and four- chamber views). Grad-CAM images of these views highlighted features that were similar to those used by experts in manual classification. Parasternal short axis subcategories (which included apex level, mitral valve level, and papillary muscle level) had accuracies of 54\%-73\%. Grad-CAM images illustrate that the network classifies most parasternal short axis views as belonging to the papillary muscle level. Likely more images and incorporating time-dependent features would increase the parasternal short axis view accuracy. Overall, a convolutional neural network can be used to reliably classify echocardiogram views.

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Classification of brain lesions on magnetic resonance imaging using superpixel, PSO and convolutional neural network

10.5753/wvc.2019.7640 ◽

2019 ◽

Author(s):

Carolina L. S. Cipriano ◽

Giovanni L. F. Da Silva ◽

Jonnison L. Ferreira ◽

Aristófanes C. Silva ◽

Anselmo Cardoso De Paiva

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Active Regions ◽

Pso Algorithm ◽

Resonance Imaging ◽

Mr Images ◽

Clinical Routine ◽

The Brain ◽

Manual Classification

One of the most severe and common brain tumors is gliomas. Manual classiﬁcation of injuries of this type is a laborious task in the clinical routine. Therefore, this work proposes an automatic method to classify lesions in the brain in 3D MR images based on superpixels, PSO algorithm and convolutional neural network. The proposed method obtained results for the complete, central and active regions, an accuracy of 87.88%, 70.51%, 80.08% and precision of 76%, 84%, 75% for the respective regions. The results demonstrate the difﬁculty of the network in the classiﬁcation of the regions found in the lesions.

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Classification and Visualisation of Normal and Abnormal Radiographs; a comparison between Eleven Convolutional Neural Network Architectures

10.1101/2021.06.16.21259014 ◽

2021 ◽

Author(s):

Ananda Ananda ◽

Kwun Ho Ngan ◽

Cefa Karabag ◽

Eduardo Alonso ◽

Alex Ter-Sarkisov ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Data Augmentation ◽

Kappa Coefficient ◽

Network Architectures ◽

Radiographic Images ◽

Cohen's Kappa ◽

Neural Network Architectures ◽

Activation Mapping

This paper investigates the classification of radiographic images with eleven convolutional neural network (CNN) architectures (GoogleNet, VGG-19, AlexNet, SqueezeNet, ResNet-18, Inception-v3, ResNet-50, VGG-16, ResNet-101, DenseNet-201 and Inception-ResNet-v2). The CNNs were used to classify a series of wrist radiographs from the Stanford Musculoskeletal Radiographs (MURA) dataset into two classes - normal and abnormal. The architectures were compared for different hyper-parameters against accuracy and Cohen's kappa coefficient. The best two results were then explored with data augmentation. Without the use of augmentation, the best results were provided by Inception-Resnet-v2 (Mean accuracy = 0.723, Mean kappa = 0.506). These were significantly improved with augmentation to Inception-Resnet-v2 (Mean accuracy = 0.857, Mean kappa = 0.703). Finally, Class Activation Mapping was applied to interpret activation of the network against the location of an anomaly in the radiographs.

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Classification and Visualisation of Normal and Abnormal Radiographs; A Comparison between Eleven Convolutional Neural Network Architectures

Sensors ◽

10.3390/s21165381 ◽

2021 ◽

Vol 21 (16) ◽

pp. 5381

Author(s):

Ananda Ananda ◽

Kwun Ho Ngan ◽

Cefa Karabağ ◽

Aram Ter-Sarkisov ◽

Eduardo Alonso ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Data Augmentation ◽

Kappa Coefficient ◽

Network Architectures ◽

Radiographic Images ◽

Cohen's Kappa ◽

Neural Network Architectures ◽

Activation Mapping

This paper investigates the classification of radiographic images with eleven convolutional neural network (CNN) architectures (GoogleNet, VGG-19, AlexNet, SqueezeNet, ResNet-18, Inception-v3, ResNet-50, VGG-16, ResNet-101, DenseNet-201 and Inception-ResNet-v2). The CNNs were used to classify a series of wrist radiographs from the Stanford Musculoskeletal Radiographs (MURA) dataset into two classes—normal and abnormal. The architectures were compared for different hyper-parameters against accuracy and Cohen’s kappa coefficient. The best two results were then explored with data augmentation. Without the use of augmentation, the best results were provided by Inception-ResNet-v2 (Mean accuracy = 0.723, Mean kappa = 0.506). These were significantly improved with augmentation to Inception-ResNet-v2 (Mean accuracy = 0.857, Mean kappa = 0.703). Finally, Class Activation Mapping was applied to interpret activation of the network against the location of an anomaly in the radiographs.

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Insect Detection and Classification Based on an Improved Convolutional Neural Network

Sensors ◽

10.3390/s18124169 ◽

2018 ◽

Vol 18 (12) ◽

pp. 4169 ◽

Cited By ~ 24

Author(s):

Denan Xia ◽

Peng Chen ◽

Bing Wang ◽

Jun Zhang ◽

Chengjun Xie

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Search Technique ◽

Factors Affecting ◽

Crop Fields ◽

The Neural Network ◽

Multi Classification ◽

Expensive Process ◽

Manual Classification

Regarding the growth of crops, one of the important factors affecting crop yield is insect disasters. Since most insect species are extremely similar, insect detection on field crops, such as rice, soybean and other crops, is more challenging than generic object detection. Presently, distinguishing insects in crop fields mainly relies on manual classification, but this is an extremely time-consuming and expensive process. This work proposes a convolutional neural network model to solve the problem of multi-classification of crop insects. The model can make full use of the advantages of the neural network to comprehensively extract multifaceted insect features. During the regional proposal stage, the Region Proposal Network is adopted rather than a traditional selective search technique to generate a smaller number of proposal windows, which is especially important for improving prediction accuracy and accelerating computations. Experimental results show that the proposed method achieves a heightened accuracy and is superior to the state-of-the-art traditional insect classification algorithms.

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LENS CLASSIFICATION ACCORDING TO THE TYPE OF LIGHT SPOT USING A NEURAL NETWORK

Automation and modeling in design and management of ◽

10.30987/2658-6436-2020-4-4-14 ◽

2020 ◽

Vol 2020 (4) ◽

pp. 4-14

Author(s):

Vladimir Budak ◽

Ekaterina Ilyina

Keyword(s):

Neural Network ◽

Light Intensity ◽

Convolutional Neural Network ◽

Deep Convolutional Neural Network ◽

Beam Angle ◽

New Model ◽

Technical Parameters ◽

Transfer Training ◽

Trained Network

The article proposes the classification of lenses with different symmetrical beam angles and offers a scale as a spot-light’s palette. A collection of spotlight’s images was created and classified according to the proposed scale. The analysis of 788 pcs of existing lenses and reflectors with different LEDs and COBs carried out, and the dependence of the axial light intensity from beam angle was obtained. A transfer training of new deep convolutional neural network (CNN) based on the pre-trained GoogleNet was performed using this collection. GradCAM analysis showed that the trained network correctly identifies the features of objects. This work allows us to classify arbitrary spotlights with an accuracy of about 80 %. Thus, light designer can determine the class of spotlight and corresponding type of lens with its technical parameters using this new model based on CCN.

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Method of determination of the text direction on the image with the use of convolutional neural network

Informatization and communication ◽

10.34219/2078-8320-2020-11-2-96-99 ◽

2020 ◽

pp. 96-99

Author(s):

P.L. Nikolaev

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Deep Neural Network ◽

Binary Classification ◽

Synthetic Data ◽

Real Data ◽

Method Of Determination ◽

Classification Of Images

This article deals with method of binary classification of images with small text on them Classification is based on the fact that the text can have 2 directions – it can be positioned horizontally and read from left to right or it can be turned 180 degrees so the image must be rotated to read the sign. This type of text can be found on the covers of a variety of books, so in case of recognizing the covers, it is necessary first to determine the direction of the text before we will directly recognize it. The article suggests the development of a deep neural network for determination of the text position in the context of book covers recognizing. The results of training and testing of a convolutional neural network on synthetic data as well as the examples of the network functioning on the real data are presented.

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A Study on Multi Class Classification from Breast Cancer Images using Ensemble Network and Transfer Learning

Recent Patents on Engineering ◽

10.2174/1872212114999201109205421 ◽

2020 ◽

Vol 14 ◽

Author(s):

Lahari Tipirneni ◽

Rizwan Patan

Keyword(s):

Breast Cancer ◽

Neural Network ◽

Convolutional Neural Network ◽

Binary Classification ◽

Disease Diagnosis ◽

Feature Descriptors ◽

Histopathological Images ◽

Viable Approach ◽

Multi Class Classification

Abstract:: Millions of deaths all over the world are caused by breast cancer every year. It has become the most common type of cancer in women. Early detection will help in better prognosis and increases the chance of survival. Automating the classification using Computer-Aided Diagnosis (CAD) systems can make the diagnosis less prone to errors. Multi class classification and Binary classification of breast cancer is a challenging problem. Convolutional neural network architectures extract specific feature descriptors from images, which cannot represent different types of breast cancer. This leads to false positives in classification, which is undesirable in disease diagnosis. The current paper presents an ensemble Convolutional neural network for multi class classification and Binary classification of breast cancer. The feature descriptors from each network are combined to produce the final classification. In this paper, histopathological images are taken from publicly available BreakHis dataset and classified between 8 classes. The proposed ensemble model can perform better when compared to the methods proposed in the literature. The results showed that the proposed model could be a viable approach for breast cancer classification.

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