scholarly journals The Crossbreed Invariant Optimization MSVM Method for Effective Diagnosis of Pneumonia from Chest X-Ray Images

2021 ◽  
Vol 10 (6) ◽  
pp. 3091-3100
Keyword(s):  
Performance Metrics ◽  
Fitness Function ◽  
Test Model ◽  
Scale Invariant ◽  
Detection Mechanism ◽  
Severe Problem ◽  
X Ray ◽  
Feature Sets ◽  
Survival Percentage ◽  
Chest X Ray

The survival percentage of pulmonary sufferers can be improved if pneumonia is detected in time. Imaging of the chest x-Ray is the most common way of finding as well as identifying pneumonia. A competent radiologist poses a severe problem while identifying pneumonia using CXR scans. To maximize classification precision, it requires an autonomous computer-aided detection approach. Designing a lightweight autonomous pneumonia detection mechanism for resource-efficient healthcare devices is critical for enhancing healthcare quality while lowering expenses and increasing reaction time. In this proposed work, a machine learning-based hybridization approach is implemented for the identification of pneumonia in the chest x-Ray scans. The proposed methodology is divided into different segments: the 1st segment is to remove noise from the chest x-Ray scans (pre-processing). After the pre-processing of CXR scans, the second module is to extract features from the pre-processed scans. The scale-invariant feature transform (SIFT) method is implemented for the extraction of essential features. This CIO-MSVM (Crossbreed Invariant Optimization-MSVM) method will select the valuable feature with the help of FF (fitness function). This function will help to select the feature matrix and then implement the MSVM algorithm. It will pass the instance selected feature set to the train model and test model. It will classify the feature sets. If feature sets will match then detect or classify the Chest X-ray image and evaluate the performance metrics such as accuracy, spec, sens., etc and compared with the existing methods.

scholarly journals Objective Evaluation of Deep Uncertainty Predictions for COVID-19 Detection

2021 ◽  
Author(s):  
Hamzeh Asgharnezhad ◽  
Afshar Shamsi ◽  
Roohallah Alizadehsani ◽  
Abbas Khosravi ◽  
Saeid Nahavandi ◽  
...  
Keyword(s):  
Uncertainty Quantification ◽  
Performance Metrics ◽  
Confusion Matrix ◽  
Objective Evaluation ◽  
X Rays ◽  
The Novel ◽  
X Ray ◽  
Uncertainty Estimates ◽  
Chest X Ray ◽  
Novel Concept

Abstract Deep neural networks (DNNs) have been widely applied for detecting COVID-19 in medical images. Existing studies mainly apply transfer learning and other data representation strategies to generate accurate point estimates. The generalization power of these networks is always questionable due to being developed using small datasets and failing to report their predictive confidence. Quantifying uncertainties associated with DNN predictions is a prerequisite for their trusted deployment in medical settings. Here we apply and evaluate three uncertainty quantification techniques for COVID-19 detection using chest X-Ray (CXR) images. The novel concept of uncertainty confusion matrix is proposed and new performance metrics for the objective evaluation of uncertainty estimates are introduced. Through comprehensive experiments, it is shown that networks pertained on CXR images outperform networks pretrained on natural image datasets such as ImageNet. Qualitatively and quantitatively evaluations also reveal that the predictive uncertainty estimates are statistically higher for erroneous predictions than correct predictions. Accordingly, uncertainty quantification methods are capable of flagging risky predictions with high uncertainty estimates. We also observe that ensemble methods more reliably capture uncertainties during the inference. DNN-based solutions for COVID-19 detection have been mainly proposed without any principled mechanism for risk mitigation. Previous studies have mainly focused on on generating single-valued predictions using pretrained DNNs. In this paper, we comprehensively apply and comparatively evaluate three uncertainty quantification techniques for COVID-19 detection using chest X-Ray images. The novel concept of uncertainty confusion matrix is proposed and new performance metrics for the objective evaluation of uncertainty estimates are introduced for the first time. Using these new uncertainty performance metrics, we quantitatively demonstrate where and when we could trust DNN predictions for COVID-19 detection from chest X-rays. It is important to note the proposed novel uncertainty evaluation metrics are generic and could be applied for evaluation of probabilistic forecasts in all classification problems.

COVID-19 Chest X-Ray Images Diagnosis: A Neutrosophic and Deep Transfer Learning Approach

2020 ◽  
Author(s):  
Nour Eldeen M. Khalifa ◽  
Florentin Smarandache ◽  
Mohamed Loey
Keyword(s):  
Deep Learning ◽  
Performance Metrics ◽  
World Health ◽  
Transfer Model ◽  
X Ray ◽  
Proposed Model ◽  
The World ◽  
Chest X Ray ◽  
Testing Accuracy ◽  
Transfer Models

Coronavirus, also known as COVID-19, has spread to several countries around the world. It was announced as a pandemic disease by The World Health Organization (WHO) in 2020 for its devastating impact on humans. With the advancements in computer science algorithms, the detection of this type of virus in the early stages is urgently needed for the fast recovery of patients. In this paper, a neutrosophic with a deep learning model for the detection of COVID-19 from chest X-ray medical digital images is presented. The proposed model relies on neutrosophic theory by converting the medical images from the grayscale spatial domain to the neutrosophic domain. The neutrosophic domain consists of three types of images and they are, the True (T) images, the Indeterminacy (I) images, and the Falsity (F) images. Using neutrosophic images has positively affected the accuracy of the proposed model. The dataset used in this research has been collected from different sources as there is no benchmark dataset for COVID-19 chest X-ray until the writing of this research. The dataset consists of four classes and they are COVID-19, Normal, Pneumonia bacterial, and Pneumonia virus. After the conversion to the neutrosophic domain, the images are fed into three different deep transfer models and they are Alexnet, Googlenet, and Restnet18. Those models are selected as they have a small number of layers on their architectures and they have been used with related work. To test the performance of the conversion to the neutrosophic domain, four scenarios have been tested. The first scenario is training the deep transfer models with True (T) neutrosophic images only. The second one is training on Indeterminacy (I) neutrosophic images, while the third scenario is training the deep models over the Falsity (F) neutrosophic images. The fourth scenario is training over the combined (T, I, F) neutrosophic images. According to the experimental results, the combined (T, I, F) neutrosophic images achieved the highest accuracy possible for the validation, testing and all performance metrics such Precision, Recall and F1 Score using Resnet18 as a deep transfer model. The proposed model achieved a testing accuracy with 78.70%. Furthermore, the proposed model using neutrosophic and Resnet18 had achieved superior testing accuracy with a related work which achieved 52.80% with the same experimental environmental setup and the same deep learning hyperparameters.

Deep Neural Network-Based Screening Model for COVID-19-Infected Patients Using Chest X-Ray Images

2020 ◽  
pp. 2151004 ◽  
Author(s):  
Dilbag Singh ◽  
Vijay Kumar ◽  
Vaishali Yadav ◽  
Manjit Kaur
Keyword(s):  
Neural Network ◽  
Performance Metrics ◽  
Automated Analysis ◽  
X Rays ◽  
X Ray ◽  
Screening Model ◽  
X Ray Imaging ◽  
Adaptive Differential Evolution ◽  
Chest X Ray ◽  
Almost All

There are limited coronavirus disease 2019 (COVID-19) testing kits, therefore, development of other diagnosis approaches is desirable. The doctors generally utilize chest X-rays and Computed Tomography (CT) scans to diagnose pneumonia, lung inflammation, abscesses, and/or enlarged lymph nodes. Since COVID-19 attacks the epithelial cells that line our respiratory tract, therefore, X-ray images are utilized in this paper, to classify the patients with infected (COVID-19 [Formula: see text]ve) and uninfected (COVID-19 [Formula: see text]ve) lungs. Almost all hospitals have X-ray imaging machines, therefore, the chest X-ray images can be used to test for COVID-19 without utilizing any kind of dedicated test kits. However, the chest X-ray-based COVID-19 classification requires a radiology expert and significant time, which is precious when COVID-19 infection is increasing at a rapid rate. Therefore, the development of an automated analysis approach is desirable to save the medical professionals’ valuable time. In this paper, a deep convolutional neural network (CNN) approach is designed and implemented. Besides, the hyper-parameters of CNN are tuned using Multi-objective Adaptive Differential Evolution (MADE). Extensive experiments are performed by considering the benchmark COVID-19 dataset. Comparative analysis reveals that the proposed technique outperforms the competitive machine learning models in terms of various performance metrics.

scholarly journals A comparative study of multiple neural network for detection of COVID-19 on chest X-ray

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Anis Shazia ◽  
Tan Zi Xuan ◽  
Joon Huang Chuah ◽  
Juliana Usman ◽  
Pengjiang Qian ◽  
...  
Keyword(s):  
Comparative Study ◽  
Healthcare Workers ◽  
Performance Metrics ◽  
Medical Image Analysis ◽  
X Ray ◽  
Research Areas ◽  
Rapid Spread ◽  
Chest X Ray ◽  
And Performance ◽  
Multiple Neural Network

AbstractCoronavirus disease of 2019 or COVID-19 is a rapidly spreading viral infection that has affected millions all over the world. With its rapid spread and increasing numbers, it is becoming overwhelming for the healthcare workers to rapidly diagnose the condition and contain it from spreading. Hence it has become a necessity to automate the diagnostic procedure. This will improve the work efficiency as well as keep the healthcare workers safe from getting exposed to the virus. Medical image analysis is one of the rising research areas that can tackle this issue with higher accuracy. This paper conducts a comparative study of the use of the recent deep learning models (VGG16, VGG19, DenseNet121, Inception-ResNet-V2, InceptionV3, Resnet50, and Xception) to deal with the detection and classification of coronavirus pneumonia from pneumonia cases. This study uses 7165 chest X-ray images of COVID-19 (1536) and pneumonia (5629) patients. Confusion metrics and performance metrics were used to analyze each model. Results show DenseNet121 (99.48% of accuracy) showed better performance when compared with the other models in this study.

scholarly journals Labelling chest x-ray reports using an open-source NLP and ML tool for text data binary classification

2019 ◽  
Author(s):  
Sohrab Towfighi ◽  
Arnav Agarwal ◽  
Denise Y. F. Mak ◽  
Amol Verma
Keyword(s):  
Internal Medicine ◽  
Language Processing ◽  
Performance Metrics ◽  
Random Search ◽  
General Medicine ◽  
X Rays ◽  
Text Data ◽  
X Ray ◽  
Radiology Reports ◽  
Chest X Ray

AbstractThe chest x-ray is a commonly requested diagnostic test on internal medicine wards which can diagnose many acute pathologies needing intervention. We developed a natural language processing (NLP) and machine learning (ML) model to identify the presence of opacities or endotracheal intubation on chest x-rays using only the radiology report. This a preliminary report of our work and findings. Using the General Medicine Inpatient Initiative (GEMINI) dataset, housing inpatient clinical and administrative data from 7 major hospitals, we retrieved 1000 plain film radiology reports which were classified according to 4 labels by an internal medicine resident. NLP/ML models were developed to identify the following on the radiograph reports: the report is that of a chest x-ray, there is definite absence of an opacity, there is definite presence of an opacity, the report is a follow-up report with minimal details in its text, and there is an endotracheal tube in place. Our NLP/ML model development methodology included a random search of either TF-IDF or bag-of-words for vectorization along with random search of various ML models. Our Python programming scripts were made publicly available on GitHub to allow other parties to train models using their own text data. 100 randomly generated ML pipelines were compared using 10-fold cross validation on 75% of the data, while 25% of the data was left out for generalizability testing. With respect to the question of whether a chest x-ray definitely lacks an opacity, the model’s performance metrics were accuracy of 0.84, precision of 0.94, recall of 0.81, and receiver operating characteristic area under curve of 0.86. Model performance was worse when trained against a highly imbalanced dataset despite the use of an advanced oversampling technique.

scholarly journals PDCOVIDNet: A Parallel-Dilated Convolutional Neural Network Architecture for Detecting COVID-19 from Chest X-Ray Images

2020 ◽  
Author(s):  
Nihad K Chowdhury ◽  
Muhtadir Rahman ◽  
Muhammad Ashad Kabir
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Performance Metrics ◽  
Detection System ◽  
Feature Maps ◽  
Screening Techniques ◽  
X Ray ◽  
Support Resistance ◽  
Chest X Ray

The COVID-19 pandemic continues to severely undermine the prosperity of the global health system. To combat this pandemic, effective screening techniques for infected patients are indispensable. There is no doubt that the use of chest X-ray images for radiological assessment is one of the essential screening techniques. Some of the early studies revealed that the patient’s chest X-ray images showed abnormalities, which is natural for patients infected with COVID-19. In this paper,we proposed a parallel-dilated convolutional neural network (CNN) based COVID-19 detection system from chest x-ray images, named as Parallel-Dilated COVIDNet (PDCOVIDNet). First, the publicly available chest X-ray collection fully preloaded and enhanced, and then classified by the proposed method. Differing convolution dilation rate in a parallel form demonstrates the proof-of-principle for using PDCOVIDNet to extract radiological features for COVID-19 detection. Accordingly, we have assisted our method with two visualization methods, which are specifically designed to increase understanding of the key components associated with COVID-19 infection. Both visualization methods compute gradients for a given image category related to feature maps of the last convolutional layer to create a class-discriminative region. In our experiment, we used a total of 2,905 chest X-ray images, comprising three cases (such as COVID-19, normal, and viral pneumonia), and empirical evaluations revealed that the proposed method extracted more significantfeatures expeditiously related to suspected disease. The experimental results demonstrate that our proposed method significantly improves performance metrics: the accuracy, precision, recall and F1 scores reach 96.58%, 96.58%, 96.59% and 96.58%, respectively, which is comparable or enhanced compared with the state-of-the-art methods. We believe that our contribution can support resistance to COVID-19, and will adopt for COVID-19 screening in AI-based systems.

scholarly journals Deep-Pneumonia Framework Using Deep Learning Models Based on Chest X-Ray Images

Diagnostics ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 649 ◽  
Author(s):  
Nada M. Elshennawy ◽  
Dina M. Ibrahim
Keyword(s):  
Deep Learning ◽  
Performance Metrics ◽  
Short Term Memory ◽  
Area Under The Curve ◽  
The Elderly ◽  
Learning Models ◽  
X Ray ◽  
Learning Framework ◽  
Chest X Ray ◽  
Long Short Term Memory

Pneumonia is a contagious disease that causes ulcers of the lungs, and is one of the main reasons for death among children and the elderly in the world. Several deep learning models for detecting pneumonia from chest X-ray images have been proposed. One of the extreme challenges has been to find an appropriate and efficient model that meets all performance metrics. Proposing efficient and powerful deep learning models for detecting and classifying pneumonia is the main purpose of this work. In this paper, four different models are developed by changing the used deep learning method; two pre-trained models, ResNet152V2 and MobileNetV2, a Convolutional Neural Network (CNN), and a Long Short-Term Memory (LSTM). The proposed models are implemented and evaluated using Python and compared with recent similar research. The results demonstrate that our proposed deep learning framework improves accuracy, precision, F1-score, recall, and Area Under the Curve (AUC) by 99.22%, 99.43%, 99.44%, 99.44%, and 99.77%, respectively. As clearly illustrated from the results, the ResNet152V2 model outperforms other recently proposed works. Moreover, the other proposed models—MobileNetV2, CNN, and LSTM-CNN—achieved results with more than 91% in accuracy, recall, F1-score, precision, and AUC, and exceed the recently introduced models in the literature.

scholarly journals COVID-19 Identification using Machine Learning Classifiers with GLCM Features of Chest X-ray Images

2021 ◽  
Vol 18 (23) ◽  
pp. 46
Author(s):  
Sudeep D. Thepade ◽  
Hrishikesh Jha
Keyword(s):  
Machine Learning ◽  
Performance Metrics ◽  
Automated System ◽  
Learning Models ◽  
X Ray ◽  
Chest X Ray ◽  
Rapid Pace ◽  
Occurrence Matrix ◽  
Machine Learning Models

COVID-19 is an ongoing pandemic, and is also known by the name coronavirus. It was originally discovered in Wuhan, China, in December, 2019. Since then, it has been increasing rapidly worldwide. Since it has been increasing at such a rapid pace, testing equipment has limited availability. Also, this disease spreads very quickly, so it is better if it is detected earlier, in order so that it can be stopped from spreading. Therefore, the importance of early detection has increased; however, because of the shortage of testing sets, it is a necessity to develop an automated system that can detect whether the COVID-19 disease is present in a person or not as early as possible. Therefore, in this work, to extract features from X-ray images of the chest, we have made use of the Gray Level Co-occurrence Matrix (GLCM). After extracting these features for the classification of the images, we used different machine learning models, and an ensemble of machine learning models, to classify X-ray images of the chest as COVID-19, Normal, Pneumonia-bac, or Pneumonia-vir. Considering the average of performance metrics, the ensemble of Random Forest-MLP gave the best result among the variations.

scholarly journals A Comparative Study of Multiple Neural Network for Detection of Covid 19 on Chest X-Ray

2021 ◽  
Author(s):  
Anis Shazia ◽  
Tan Zi Xuan ◽  
Joon Huang Chuah ◽  
Juliana Usman ◽  
Pengjiang Qian ◽  
...  
Keyword(s):  
Comparative Study ◽  
Healthcare Workers ◽  
Performance Metrics ◽  
Medical Image Analysis ◽  
X Ray ◽  
Research Areas ◽  
Rapid Spread ◽  
Chest X Ray ◽  
And Performance ◽  
Multiple Neural Network

Abstract Coronavirus disease of 2019 or Covid-19 is a rapidly spreading viral infection that has affected millions all over the world. With its rapid spread and increasing numbers it is becoming over-whelming for the healthcare workers ta rapidly diagnose the condition and contain it from spreading. Hence it has become a necessity to automate the diagnostic procedure. This will improve the work efficiency as well as keep the healthcare workers safe from getting exposed to the virus. Medical image analysis is one of the rising research areas that can tackle this issue with higher accuracy. This paper conducts a comparative study of the use of the recent deep learning models (VGG16, VGG19, DenseNet121, Inception-ResNet-V2, InceptionV3, Resnet50, and Xception) to deal with detection and classification of coronavirus pneumonia from other pneumonia cases. This study uses 7,165 chest X-ray images of Covid-19 (1536) and Pneumonia (5629) patients. Confusion metrics and performance metrics were used to analyze each model. Results show DenseNet121 (99.48% of accuracy) showed better performance when compared with the other models in this study.

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