scholarly journals ModCOVNN: a convolutional neural network approach in COVID-19 prognosis

2021 ◽  
Vol 7 (2) ◽  
pp. 125
Author(s):  
Ahmed Wasif Reza ◽  
Jannatul Ferdous Sorna ◽  
Md. Momtaz Uddin Rashel ◽  
Mir Moynuddin Ahmed Shibly
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Primary Concern ◽  
Neural Network Approach ◽  
Good Efficiency ◽  
X Ray ◽  
Proposed Model ◽  
Driven System ◽  
History Of ◽  
Pcr Test

COVID-19 is a devastating pandemic in the history of humankind. It is a highly contagious flu that can spread from human to human. For being so contagious, detecting patients with it and isolating them has become the primary concern for healthcare professionals. However, identifying COVID-19 patients with a Polymerase chain reaction (PCR) test can sometimes be problematic and time-consuming. Therefore, detecting patients with this virus from X-ray chest images can be a perfect alternative to the de-facto standard PCR test. This article aims at providing such a decision support system that can detect COVID-19 patients with the help of X-ray images. To do that, a novel convolutional neural network (CNN) based architecture, namely ModCOVNN, has been introduced. To determine whether the proposed model works with good efficiency, two CNN-based architectures – VGG16 and VGG19 have been developed for the detection task. The experimental results of this study have proved that the proposed architecture has outperformed the other two models with 98.08% accuracy, 98.14% precision, and 98.4% recall. This result indicates that proper detection of COVID-19 patients with the help of X-ray images of the chest is possible using machine learning methods with high accuracy. This type of data-driven system can help us to overcome the current appalling situation throughout the world.

scholarly journals An Effective Convolutional Neural Network Model for the Early Detection of COVID-19 Using Chest X-ray Images

2021 ◽  
Vol 11 (21) ◽  
pp. 10301
Author(s):  
Muhammad Shoaib Farooq ◽  
Attique Ur Rehman ◽  
Muhammad Idrees ◽  
Muhammad Ahsan Raza ◽  
Jehad Ali ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Real World ◽  
Early Stage ◽  
Binary Classification ◽  
X Rays ◽  
X Ray ◽  
Proposed Model ◽  
Chest X Ray

COVID-19 has been difficult to diagnose and treat at an early stage all over the world. The numbers of patients showing symptoms for COVID-19 have caused medical facilities at hospitals to become unavailable or overcrowded, which is a major challenge. Studies have recently allowed us to determine that COVID-19 can be diagnosed with the aid of chest X-ray images. To combat the COVID-19 outbreak, developing a deep learning (DL) based model for automated COVID-19 diagnosis on chest X-ray is beneficial. In this research, we have proposed a customized convolutional neural network (CNN) model to detect COVID-19 from chest X-ray images. The model is based on nine layers which uses a binary classification method to differentiate between COVID-19 and normal chest X-rays. It provides COVID-19 detection early so the patients can be admitted in a timely fashion. The proposed model was trained and tested on two publicly available datasets. Cross-dataset studies are used to assess the robustness in a real-world context. Six hundred X-ray images were used for training and two hundred X-rays were used for validation of the model. The X-ray images of the dataset were preprocessed to improve the results and visualized for better analysis. The developed algorithm reached 98% precision, recall and f1-score. The cross-dataset studies also demonstrate the resilience of deep learning algorithms in a real-world context with 98.5 percent accuracy. Furthermore, a comparison table was created which shows that our proposed model outperforms other relative models in terms of accuracy. The quick and high-performance of our proposed DL-based customized model identifies COVID-19 patients quickly, which is helpful in controlling the COVID-19 outbreak.

scholarly journals The Combination of Adaptive Convolutional Neural Network and Bag of Visual Words in Automatic Diagnosis of Third Molar Complications on Dental X-Ray Images

Diagnostics ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 209 ◽  
Author(s):  
Vo Truong Nhu Ngoc ◽  
Agwu Chinedu Agwu ◽  
Le Hoang Son ◽  
Tran Manh Tuan ◽  
Cu Nguyen Giap ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Third Molar ◽  
Quality Prediction ◽  
X Rays ◽  
Medical Practitioners ◽  
X Ray ◽  
Visual Words ◽  
Proposed Model ◽  
Defect Recognition

In dental diagnosis, recognizing tooth complications quickly from radiology (e.g., X-rays) takes highly experienced medical professionals. By using object detection models and algorithms, this work is much easier and needs less experienced medical practitioners to clear their doubts while diagnosing a medical case. In this paper, we propose a dental defect recognition model by the integration of Adaptive Convolution Neural Network and Bag of Visual Word (BoVW). In this model, BoVW is used to save the features extracted from images. After that, a designed Convolutional Neural Network (CNN) model is used to make quality prediction. To evaluate the proposed model, we collected a dataset of radiography images of 447 patients in Hanoi Medical Hospital, Vietnam, with third molar complications. The results of the model suggest accuracy of 84% ± 4%. This accuracy is comparable to that of experienced dentists and radiologists.

scholarly journals Automated hippocampal segmentation improved by convolutional neural network approach in participants with a history of cerebrovascular accident

2020 ◽  
Vol 16 (S4) ◽  
Author(s):  
Artemis Zavaliangos‐Petropul ◽  
Meral A Tubi ◽  
Alyssa Zhu ◽  
Elizabeth Haddad ◽  
Neda Jahanshad ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Cerebrovascular Accident ◽  
Network Approach ◽  
Neural Network Approach ◽  
History Of

scholarly journals A convolutional neural network approach to calibrating the rotation axis for X-ray computed tomography

2017 ◽  
Vol 24 (2) ◽  
pp. 469-475 ◽  
Author(s):  
Xiaogang Yang ◽  
Francesco De Carlo ◽  
Charudatta Phatak ◽  
Dogˇa Gürsoy
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Rotation Axis ◽  
Synthetic Data ◽  
Neural Network Approach ◽  
X Ray ◽  
Swiss Light Source ◽  
Machine Learning Approach ◽  
X Ray Computed ◽  
Photon Source

This paper presents an algorithm to calibrate the center-of-rotation for X-ray tomography by using a machine learning approach, the Convolutional Neural Network (CNN). The algorithm shows excellent accuracy from the evaluation of synthetic data with various noise ratios. It is further validated with experimental data of four different shale samples measured at the Advanced Photon Source and at the Swiss Light Source. The results are as good as those determined by visual inspection and show better robustness than conventional methods. CNN has also great potential for reducing or removing other artifacts caused by instrument instability, detector non-linearity, etc. An open-source toolbox, which integrates the CNN methods described in this paper, is freely available through GitHub at tomography/xlearn and can be easily integrated into existing computational pipelines available at various synchrotron facilities. Source code, documentation and information on how to contribute are also provided.

Chest X-Ray Investigation: A Convolutional Neural Network Approach

2020 ◽  
Vol 45 ◽  
pp. 57-70
Author(s):  
Tapan K. Das ◽  
Chiranji Lal Chowdhary ◽  
X.Z. Gao
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Rural Areas ◽  
Neural Network Approach ◽  
X Ray ◽  
Remote Areas ◽  
Arduous Task ◽  
Number Of Patients ◽  
Chest X Ray ◽  
Aided Diagnosis

Though India being home of one out of every six people in the globe, is facing an arduous task of providing healthcare service, especially to the large number of patients in remote areas due to lack of diagnosis support systems and doctors. It is reported that hospitals in rural areas have an insufficient radiologist due to which thousands of cases are usually handled by single doctor. In this context, we aim to develop an AI based computer-aided diagnosis tool, which can classify abnormalities by reading chest X-ray so that it could assist the doctors in arriving at quick diagnosis. We have employed a Convolutional Neural Network (CNN) designed by Google known as XceptionNet to detect those pathologies in ChestX-ray14 data. Further, same data is being used for executing other CNN- ResNet. Finally, both the results obtained are compared to assess the superior CNN model for X-ray level diagnosis.

scholarly journals Implementation of convolutional neural network approach for COVID-19 disease detection

2020 ◽  
Vol 52 (12) ◽  
pp. 590-601
Author(s):  
Emrah Irmak
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Clinical Data ◽  
Data Sets ◽  
Grid Search ◽  
Neural Network Approach ◽  
Data Set ◽  
X Ray ◽  
Average Accuracy ◽  
Chest X Ray

In this paper, two novel, powerful, and robust convolutional neural network (CNN) architectures are designed and proposed for two different classification tasks using publicly available data sets. The first architecture is able to decide whether a given chest X-ray image of a patient contains COVID-19 or not with 98.92% average accuracy. The second CNN architecture is able to divide a given chest X-ray image of a patient into three classes (COVID-19 versus normal versus pneumonia) with 98.27% average accuracy. The hyperparameters of both CNN models are automatically determined using Grid Search. Experimental results on large clinical data sets show the effectiveness of the proposed architectures and demonstrate that the proposed algorithms can overcome the disadvantages mentioned above. Moreover, the proposed CNN models are fully automatic in terms of not requiring the extraction of diseased tissue, which is a great improvement of available automatic methods in the literature. To the best of the author’s knowledge, this study is the first study to detect COVID-19 disease from given chest X-ray images, using CNN, whose hyperparameters are automatically determined by the Grid Search. Another important contribution of this study is that it is the first CNN-based COVID-19 chest X-ray image classification study that uses the largest possible clinical data set. A total of 1,524 COVID-19, 1,527 pneumonia, and 1524 normal X-ray images are collected. It is aimed to collect the largest number of COVID-19 X-ray images that exist in the literature until the writing of this research paper.

scholarly journals Concatenation Technique in Convolutional Neural Networks for COVID-19 Detection Based on X-ray Images

2020 ◽  
Author(s):  
Yakoop Razzaz Hamoud Qasim ◽  
Habeb Abdulkhaleq Mohammed Hassan ◽  
Abdulelah Abdulkhaleq Mohammed Hassan
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Convolutional Neural Networks ◽  
X Ray ◽  
Proposed Model ◽  
Sensitivity Specificity ◽  
Better Than

In this paper we present a Convolutional Neural Network consisting of NASNet and MobileNet in parallel (concatenation) to classify three classes COVID-19, normal and pneumonia, depending on a dataset of 1083 x-ray images divided into 361 images for each class. VGG16 and RESNet152-v2 models were also prepared and trained on the same dataset to compare performance of the proposed model with their performance. After training the networks and evaluating their performance, an overall accuracy of 96.91%for the proposed model, 92.59% for VGG16 model and 94.14% for RESNet152. We obtained accuracy, sensitivity, specificity and precision of 99.69%, 99.07%, 100% and 100% respectively for the proposed model related to the COVID-19 class. These results were better than the results of other models. The conclusion, neural networks are built from models in parallel are most effective when the data available for training are small and the features of different classes are similar.

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