Survey on Design of Cardiovascular Disease Classification and Detection using Neural Network

2019 ◽  
Author(s):  
Neerajkumar S. Sathawane ◽  
Ulhaskumar M. Gokhale.
Keyword(s):  
Neural Network ◽  
Cardiovascular Disease ◽  
Disease Classification

scholarly journals Arrhythmia Classification Using One Dimensional Conventional Neural Network

2021 ◽  
Vol 13 (3) ◽  
pp. 43-58
Author(s):  
Sarah kamil ◽  
Lamia Muhammed
Keyword(s):  
Neural Network ◽  
Cardiovascular Disease ◽  
Deep Learning ◽  
Disease Classification ◽  
Visual Interpretation ◽  
Deep Convolutional Neural Networks ◽  
Bundle Branch Block ◽  
Ecg Signals ◽  
Short Period ◽  
Electrocardiogram Ecg

Arrhythmia is a heart condition that occurs due to abnormalities in the heartbeat, which means that the heart's electrical signals do not work properly, resulting in an irregular heartbeat or rhythm and thus defeating the pumping of blood. Some cases of arrhythmia are not considered serious, while others are very dangerous, life-threatening, and cause death in a short period of time. In the clinical routine, cardiac arrhythmia detection is performed by electrocardiogram (ECG) signals. The ECG is a significant diagnosis tool that is used to record the electrical activity of the heart, and its signals can reveal abnormal heart activity. However, because of their small amplitude and duration, visual interpretation of ECG signals is difficult. As a result, we present a significant approach for identifying arrhythmias using ECG signals. In this study, we proposed an approach based on Deep Learning (DL) technology that is a framework of nine-layer one-dimension Conventional Neural Network (1D CNN) for classifying automatically ECG signals into four cardiac conditions named: normal (N), Atrial Premature Beat (APB), Left Bundle Branch Block (LBBB), and Right Bundle Branch Block (RBBB). The practical test of this work was executed with the benchmark MIT-BIH database. We achieved an average accuracy of 99%, precision of 98%, recall of 96.5%, specificity of 99.08%, and an F1-score of 95.75%. The obtained results were compared with some relevant models, and they showed that the proposed framework outperformed those models in some measures. The new approach’s performance indicates its success. Also, it has been shown that deep convolutional neural networks can be used efficiently in automated detection and, therefore, cardiovascular disease protection as well as help cardiologists in medical practice by saving time and effort. Keywords: 1-D CNN, Arrhythmia, Cardiovascular Disease, Classification, Deep learning, Electrocardiogram(ECG), MIT-BIH arrhythmia database.

Artificial Neural Network for Cardiovascular Disease Prediction

2021 ◽  
Vol 9 (8) ◽  
pp. 2189-2193
Author(s):  
Abhay Patil
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Disease Classification ◽  
Disease Prediction ◽  
Intelligence Gathering ◽  
Binary Model ◽  
Cardio Vascular Disease ◽  
Artificial Neural ◽  
Cardio Vascular ◽  
The Ideal

Abstract: The assurance of coronary ailment a large part of the time depends upon an eccentric mix of clinical and masochist data. Considering this multifaceted nature, there exists a ton of income among clinical specialists and experts with respect to the useful and careful assumption for coronary sickness. In this paper, we cultivate a coronary disease prediction system that can help clinical specialists in expecting coronary ailment status reliant upon the clinical data of patients. Man-made intelligence-gathering strategies are amazingly useful in the clinical field by giving accurate results and quick finishes of ailments. Thusly, these techniques save part of the ideal opportunity for the two trained professionals and patients. The neural associations can be used as classifiers to expect the assurance of Cardiovascular Heart disorder. Keywords: Cardio Vascular disease, Classification, Artificial neural network, Categorical model and Binary model

scholarly journals Convolutional Neural Networks for Leaf Image-Based Plant Disease Classification

2019 ◽  
Vol 8 (4) ◽  
pp. 328
Author(s):  
Sachin B. Jadhav
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Convolutional Neural Networks ◽  
Plant Diseases ◽  
Experimental Results ◽  
Disease Classification ◽  
Soybean Leaves ◽  
Soybean Diseases ◽  
Validation Strategy

<span lang="EN-US">Plant pathologists desire soft computing technology for accurate and reliable diagnosis of plant diseases. In this study, we propose an efficient soybean disease identification method based on a transfer learning approach by using a pre-trained convolutional neural network (CNN’s) such as AlexNet, GoogleNet, VGG16, ResNet101, and DensNet201. The proposed convolutional neural networks were trained using 1200 plant village image dataset of diseased and healthy soybean leaves, to identify three soybean diseases out of healthy leaves. Pre-trained CNN used to enable a fast and easy system implementation in practice. We used the five-fold cross-validation strategy to analyze the performance of networks. In this study, we used a pre-trained convolutional neural network as feature extractors and classifiers. The experimental results based on the proposed approach using pre-trained AlexNet, GoogleNet, VGG16, ResNet101, and DensNet201 networks achieve an accuracy of 95%, 96.4 %, 96.4 %, 92.1%, 93.6% respectively. The experimental results for the identification of soybean diseases indicated that the proposed networks model achieves the highest accuracy</span>

scholarly journals Calcification detection using convolutional neural network architectures in Intravascular ultrasound images

2020 ◽  
Vol 17 (3) ◽  
pp. 1313
Author(s):  
Hannah Sofian ◽  
Joel Than Chia Ming ◽  
Suraya Muhammad ◽  
Norliza Mohd Noor
Keyword(s):  
Neural Network ◽  
Cardiovascular Disease ◽  
Coronary Artery ◽  
Convolutional Neural Network ◽  
Intravascular Ultrasound ◽  
Predictive Value ◽  
Area Under Curve ◽  
Ultrasound Image ◽  
Network Architectures ◽  
Neural Network Architectures

<p>Cardiovascular disease is the highest leading to death for Non-Communicable disease. Coronary artery calcification disease is part of cardiovascular disease. The built-in of the plaques and the calcification in the coronary artery inner wall make the blood vessel cross-section area narrow. The standard practice by the radiologists and medical clinical are by visual inspection to detect the calcification in the intravascular ultrasound image. Deep learning is the current image processing methods that have high potential to detect calcification analysis using convolutional neural network architecture and classifiers. To detect the absence of calcification and presence calcification on the intravascular ultrasound image, using k-fold =10, we compared the three types of convolutional neural network architectures and the seven types of classifiers with the provided ground truth from MICCAI 2011. We used two types of images named as Cartesian Coordinates image and polar reconstructed coordinate image. The classifiers such as Support Vector Machine, Discriminant analysis, Ensembles and Error-Correcting Output Codes obtained the perfect result with value one for Area Under Curve and all the performance measure result, accuracy, sensitivity, specificity, positive predictive value and negative predictive value. Area Under Curve for Naïve Bayes classifier is 0.9967 and for Decision Tree classifier is 0.9994, obtained using the polar reconstructed coordinate image for InceptionresNet-V2 architecture.</p>

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