COVID-19 and Pneumonia Diagnosis in X-Ray Images Using Convolutional Neural Networks (original) (raw)
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Diagnostic of COVID-19 Pneumonia through Convolutional Neural Networks Using Chest X-RAY
Colombo Journal of Multi-Disciplinary Research, 2023
This Study to emphasizes the need for improved diagnostic protocols and increased awareness to effectively manage COVID-19 and its complications, particularly pneumonia, to alleviate the burden on healthcare systems, underscores the critical importance of early identification of COVID-19 pneumonia as a strategic approach to mitigate devastating impact and fast detection of underlying symptoms. Introducing a novel model for detecting COVID-19 pneumonia, utilizing chest X-ray images available on open-source platform, and convolutional neural networks, enabling precise diagnosis in binary classification settings. Two steps followed to enhance classification accuracy and avoid Overfitting: (1) enlarging the data set while maintaining the balance of the classification scenarios; (2) incorporating regularization techniques and performing hyper-parameter optimization. The model is ideal for deployed locally with limited capacities and without an Internet access. Because of the network size, the model capacity reduced immensely. Comparison to literature, the final model performed better and required a disproportionately higher parameters while reaching a classification accuracy of 99.63% and model sensitivity of 93.75% for the binary cases. The models can be uploaded to a digital platform for quick diagnosis and make up for lack of professionals, and RT-PCR (reverse transcription polymerase chain reaction).
Convolutional neural network for diagnosis of viral pneumonia and COVID ‐19 alike diseases
Expert Systems, 2021
Reverse-Transcription Polymerase Chain Reaction (RT-PCR) method is currently the gold standard method for detection of viral strains in human samples, but this technique is very expensive, take time and often leads to misdiagnosis. The recent outbreak of COVID-19 has led scientists to explore other options such as the use of artificial intelligence driven tools as an alternative or a confirmatory approach for detection of viral pneumonia. In this paper, we utilized a Convolutional Neural Network (CNN) approach to detect viral pneumonia in x-ray images using a pretrained AlexNet model thereby adopting a transfer learning approach. The dataset used for the study was obtained in the form of optical Coherence Tomography and chest X-ray images made available by Kermany et al. (2018, https://doi.org/10.17632/rscbjbr9sj.3) with a total number of 5853 pneumonia (positive) and normal (negative) images. To evaluate the average efficiency of the model, the dataset was split into on 50:50, 60:40, 70:30, 80:20 and 90:10 for training and testing respectively. To evaluate the performance of the model, 10 K Cross-validation was carried out. The performance of the model using overall dataset was compared with the means of cross-validation and the currents state of arts. The classification model has shown high performance in terms of accuracy, sensitivity and specificity. 70:30 split performed better compare to other splits with accuracy of 98.73%, sensitivity of 98.59% and specificity of 99.84%.
Pneumonia and COVID-19 Detection using Convolutional Neural Networks
2020 Third International Conference on Vocational Education and Electrical Engineering (ICVEE)
COVID-19 also known as Severe Acute Respiratory Syndrome Corona virus-2 is a contagious disease that is released from tiny droplets containing saliva or mucus from respiratory system of a diseased person who talks, sneeze, or cough. It spreads rapidly through close contact with somebody who is infected or tapping or holding a virus contaminated objects and surfaces. Another infectious illness known as Pneumonia is often caused by infection due to a bacterium in the alveoli of lungs. When an infected tissue of the lungs has inflammation, it builds-up pus in it. To find out if the patient has these diseases, experts conduct physical exams and diagnose their patients through Chest X-ray, ultrasound, or biopsy of lungs. Misdiagnosis, inaccurate treatment, and if the disease is ignored will lead to the patient's loss of life. The progression of Deep Learning contributes to aid in the decisionmaking process of experts to diagnose patients with these diseases. The study employs a flexible and efficient approach of deep learning applying the model of CNN in predicting and detecting a patient unaffected and affected with the disease employing a chest X-ray image. The study utilized a collected dataset of 20,000 images using a 224x224 image resolution with 32 batch size is applied to prove the performance of the CNN model being trained. The trained-model produced an accuracy rate of 95% during the performance training. Based on the result of testing conducted, the research study can detect and predict COVID-19, bacterial, and viral-pneumonia diseases based on chest X-ray images.
Signal, Image Processing and Embedded Systems Trends
The novel coronavirus disease (COVID-19) is a highly contagious infectious disease. Even though there is a large pool of articles that showed the potential of using chest X-ray images in COVID-19 detection, a detailed study using a wide range of pre-trained convolutional neural network (CNN) encoders-based deep learning framework in screening viral, bacterial, and COVID-19 pneumonia are still missing. Deep learning network training is challenging without a properly annotated huge database. Transfer learning is a crucial technique for transferring knowledge from real-world object classification tasks to domain-specific tasks, and it may offer a viable answer. Although COVID-19 infection on the lungs and bacterial and viral pneumonia shares many similarities, they are treated differently. Therefore, it is crucial to appropriately diagnose them. The authors have compiled a large X-ray dataset (QU-MLG-COV) consisting of 16,712 CXR images with 8851 normal, 3616 COVID-19, 1485 viral, and ...
2020
COVID-19 (also known as 2019 Novel Coronavirus) first emerged in Wuhan, China and spread across the globe with unprecedented effect and has now become the greatest crisis of the modern era. The COVID-19 has proved much more pervasive demands for diagnosis that has driven researchers to develop more intelligent, highly responsive and efficient detection methods. In this work, we focus on proposing AI tools that can be used by radiologists or healthcare professionals to diagnose COVID-19 cases in a quick and accurate manner. However, the lack of a publicly available dataset of X-ray and CT images makes the design of such AI tools a challenging task. To this end, this study aims to build a comprehensive dataset of X-rays and CT scan images from multiple sources as well as provides a simple but an effective COVID-19 detection technique using deep learning and transfer learning algorithms. In this vein, a simple convolution neural network (CNN) and modified pre-trained AlexNet model are ...
International Journal For Research In Applied Science & Engineering Technology, 2020
The ongoing novel corona virus has spread all over the world and became a pandemic. This pandemic situation has led to a major crisis in healthcare systems and the global economy. As Covid-19 positive patient's increasing day by day, the crucial task is to detect and monitor disease efficiently and facilitate the results of Covid-19 positive patients to cure them as soon as possible. Currently used RT-PCR (Reverse transcription-polymerase chain reaction) testing method act as a goldmine for detecting Covid-19. But the total turnaround time required for Disease diagnosis is very large. This long turnaround time sometimes leads to patient deaths. To avoid that and detecting Covid-19 positive patients in a less time ,author proposed a method in this paper that uses Chest x-ray images for patient diagnosis and disease classification. Deep learning architecture called Convolutional neural network helps in diagnosis of patient. The tremendous success of the Convolutional neural network at image processing tasks in recent years extremely increased the use of electronic medical records and diagnostic imaging. To train and test the neural model the paper used a publicly available dataset that contains COVID-19, pneumonia, and normal patient Chest X-ray images. Also for experimental analysis a CovidNet20, Convolutional architecture was developed for disease classification along with transfer learning DenseNet121 pretrained model used for training and testing of the classification model. The proposed model able to differentiate COVID-19 and normal images as binary classification with 100% and 99% accuracy on DenseNet121 and CovidNet20 model. And, on multiclass classification with COVID-19, Normal and Pneumonia as classes Densenet121 gives 97% and CovidNet20 gives 98% accuracy.
An automatic approach based on CNN architecture to detect Covid-19 disease from chest X-ray images
Applied Intelligence
Novel coronavirus (COVID-19) is started from Wuhan (City in China), and is rapidly spreading among people living in other countries. Today, around 215 countries are affected by COVID-19 disease. WHO announced approximately number of cases 11,274,600 worldwide. Due to rapidly rising cases daily in the hospitals, there are a limited number of resources available to control COVID-19 disease. Therefore, it is essential to develop an accurate diagnosis of COVID-19 disease. Early diagnosis of COVID-19 patients is important for preventing the disease from spreading to others. In this paper, we proposed a deep learning based approach that can differentiate COVID-19 disease patients from viral pneumonia, bacterial pneumonia, and healthy (normal) cases. In this approach, deep transfer learning is adopted. We used binary and multi-class dataset which is categorized in four types for experimentation: (i) Collection of 728 X-ray images including 224 images with confirmed COVID-19 disease and 504 normal condition images (ii) Collection of 1428 X-ray images including 224 images with confirmed COVID-19 disease, 700 images with confirmed common bacterial pneumonia, and 504 normal condition images. (iii) Collections of 1442 X-ray images including 224 images with confirmed COVID-19 disease, 714 images with confirmed bacterial and viral pneumonia, and 504 images of normal conditions (iv) Collections of 5232 X-ray images including 2358 images with confirmed bacterial and 1345 with viral pneumonia, and 1346 images of normal conditions. In this paper, we have used nine convolutional neural network based architecture (AlexNet, GoogleNet, ResNet-50, Se-ResNet-50, DenseNet121, Inception V4, Inception ResNet V2, ResNeXt-50, and Se-ResNeXt-50). Experimental results indicate that the pre trained model Se-ResNeXt-50 achieves the highest classification accuracy of 99.32% for binary class and 97.55% for multi-class among all pre-trained models.
2020
Novel coronavirus pneumonia (COVID-19) is a contagious disease that has already caused thousands of deaths and infected millions of people worldwide. Thus, all technological gadgets that allow the fast detection of COVID-19 infection with high accuracy can offer help to healthcare professionals. This study is purposed to explore the effectiveness of artificial intelligence (AI) in the rapid and reliable detection of COVID-19 based on chest X-ray imaging. In this study, reliable pre-trained deep learning algorithms were applied to achieve the automatic detection of COVID-19-induced pneumonia from digital chest X-ray images.Moreover, the study aims to evaluate the performance of advanced neural architectures proposed for the classification of medical images over recent years. The data set used in the experiments involves 274 COVID-19 cases, 380 viral pneumonia, and 380 healthy cases, which was derived from several open sources of X-Rays, and the data available online. The confusion ma...
A deep learning approach for COVID-19 and pneumonia detection from chest X-ray images
International Journal of Electrical and Computer Engineering (IJECE), 2022
There has been a surge in biomedical imaging technologies with the recent advancement of deep learning. It is being used for diagnosis from X-ray, computed tomography (CT) scan, electrocardiogram (ECG), and electroencephalography (EEG) images. However, most of them are solely for particular disease detection. In this research, a computer-aided deep learning model named COVID-CXDNetV2 has been presented to detect two separate diseases, coronavirus disease 2019 (COVID-19) and pneumonia, from the X-ray images in real-time. The proposed model is made based on you only look once (YOLOv2) with residual neural network (ResNet) and trained by a vast X-ray images dataset containing 3788 samples of three classes named COVID-19 pneumonia and normal. The model has obtained the maximum overall classification accuracy of 97.9% with a loss of 0.052 for multiclass classification (COVID-19, pneumonia, and normal) and 99.8% accuracy, 99.52% sensitivity, 100% specificity with a loss of 0.001 for binary classification (COVID-19 and normal), which beats some current state-of-the-art results. Authors believe that this method will be applicable in the medical domain for the diagnosis and will significantly contribute to real life.
COVID-19 pneumonia level detection using deep learning algorithm and transfer learning
Evolutionary Intelligence
The first COVID-19 confirmed case was reported in Wuhan, China, and spread across the globe with an unprecedented impact on humanity. Since this pandemic requires pervasive diagnosis, developing smart, fast, and efficient detection techniques is significant. To this end, we have developed an Artificial Intelligence engine to classify the lung inflammation level (mild, progressive, severe stage) of the COVID-19 confirmed patient. In particular, the developed model consists of two phases; in the first phase, we calculate the volume and density of lesions and opacities of the CT scan images of the confirmed COVID-19 patient using Morphological approaches. The second phase classifies the pneumonia level of the confirmed COVID-19 patient. We use a modified Convolution Neural Network (CNN) and k-Nearest Neighbor; we also compared the results of both models to the other classification algorithms to precisely classify lung inflammation. The experiments show that the CNN model can provide testing accuracy up to 95.65% compared with exiting classification techniques. The proposed system in this work can be applied efficiently to CT scan and X-ray image datasets. Also, in this work, the Transfer Learning technique has been used to train the pre-trained modified CNN model on a smaller dataset than the original dataset; the modified CNN achieved 92.80% of testing accuracy for detecting pneumonia on chest X-ray images for the relatively extensive dataset.