CIDC-Net: Chest-X Ray Image based Disease Classification Network using Deep Learning

Recently, COVID-19 is spreading rapidly and fast detection of COVID-19 can save millions of lives. Further, the COVID-19 can be detected easily from chest x ray (CXR) images using artificial intelligence methods. However, the performance of these application and methods are reduced due to noises presented in the CXR images, which degrading the performance of overall systems. Therefore, this article is focused on implementation of an innovative method for quickly processing CXR images of low quality, which enhances the contrast using fuzzy logic. This method makes use of tuned fuzzy intensification operators and is intended to speed up the processing time. Therefore, this work is focused on implementation of CXR image-based disease classification network (CIDC-Net) for identification of COVID-19 and pneumonia related 21 diseases. The CIDC-Net utilizes the deep learning convolutional neural network (CNN) model for training and testing. Finally, the simulations revealed that the proposed CIDC-Net resulted in superior performance as compared to existing models. © 2022 IEEE.

COVID-19 chest X-ray image classification in the presence of noisy labels.

The Corona Virus Disease 2019 (COVID-19) has been declared a worldwide pandemic, and a key method for diagnosing COVID-19 is chest X-ray imaging. The application of convolutional neural network with medical imaging helps to diagnose the disease accurately, where the label quality plays an important role in the classification problem of COVID-19 chest X-rays. However, most of the existing classification methods ignore the problem that the labels are hardly completely true and effective, and noisy labels lead to a significant degradation in the performance of image classification frameworks. In addition, due to the wide distribution of lesions and the large number of local features of COVID-19 chest X-ray images, existing label recovery algorithms have to face the bottleneck problem of the difficult reuse of noisy samples. Therefore, this paper introduces a general classification framework for COVID-19 chest X-ray images with noisy labels and proposes a noisy label recovery algorithm based on subset label iterative propagation and replacement (SLIPR). Specifically, the proposed algorithm first obtains random subsets of the samples multiple times. Then, it integrates several techniques such as principal component analysis, low-rank representation, neighborhood graph regularization, and k-nearest neighbor for feature extraction and image classification. Finally, multi-level weight distribution and replacement are performed on the labels to cleanse the noise. In addition, for the label-recovered dataset, high confidence samples are further selected as the training set to improve the stability and accuracy of the classification framework without affecting its inherent performance. In this paper, three typical datasets are chosen to conduct extensive experiments and comparisons of existing algorithms under different metrics. Experimental results on three publicly available COVID-19 chest X-ray image datasets show that the proposed algorithm can effectively recover noisy labels and improve the accuracy of the image classification framework by 18.9% on the Tawsifur dataset, 19.92% on the Skytells dataset, and 16.72% on the CXRs dataset. Compared to the state-of-the-art algorithms, the gain of classification accuracy of SLIPR on the three datasets can reach 8.67%-19.38%, and the proposed algorithm also has certain scalability while ensuring data integrity.

A novel multimodal fusion framework for early diagnosis and accurate classification of COVID-19 patients using X-ray images and speech signal processing techniques.

BACKGROUND AND OBJECTIVE: COVID-19 outbreak has become one of the most challenging problems for human being. It is a communicable disease caused by a new coronavirus strain, which infected over 375 million people already and caused almost 6 million deaths. This paper aims to develop and design a framework for early diagnosis and fast classification of COVID-19 symptoms using multimodal Deep Learning techniques. METHODS: we collected chest X-ray and cough sample data from open source datasets, Cohen and datasets and local hospitals. The features are extracted from the chest X-ray images are extracted from chest X-ray datasets. We also used cough audio datasets from Coswara project and local hospitals. The publicly available Coughvid DetectNow and Virufy datasets are used to evaluate COVID-19 detection based on speech sounds, respiratory, and cough. The collected audio data comprises slow and fast breathing, shallow and deep coughing, spoken digits, and phonation of sustained vowels. Gender, geographical location, age, preexisting medical conditions, and current health status (COVID-19 and Non-COVID-19) are recorded. RESULTS: The proposed framework uses the selection algorithm of the pre-trained network to determine the best fusion model characterized by the pre-trained chest X-ray and cough models. Third, deep chest X-ray fusion by discriminant correlation analysis is used to fuse discriminatory features from the two models. The proposed framework achieved recognition accuracy, specificity, and sensitivity of 98.91%, 96.25%, and 97.69%, respectively. With the fusion method we obtained 94.99% accuracy. CONCLUSION: This paper examines the effectiveness of well-known ML architectures on a joint collection of chest-X-rays and cough samples for early classification of COVID-19. It shows that existing methods can effectively used for diagnosis and suggesting that the fusion learning paradigm could be a crucial asset in diagnosing future unknown illnesses. The proposed framework supports health informatics basis on early diagnosis, clinical decision support, and accurate prediction.

Fibonacci Numbers as Hyperparameters for Image Dimension of a Convolutional Neural Network Image Prognosis Classification Model of COVID X-ray Images

In recent years, convolutional neural networks (CNNs) have achieved amazing success in a variety of image categorization tasks. However, the architecture of CNNs has a significant impact on their performance. The designs of the most cutting-edge CNNs are frequently hand-crafted by experts in both CNNs and the topics under investigation. As a result, it's tough for users who don't have a lot of experience with CNNs to come up with the best CNN architecture for their individual image categorization challenges. This work investigates the application of the Fibonacci numbers to efficiently solve picture classification challenges by utilizing the hyperparameter of image dimension of COVID and non-COVID x-ray images. The suggested algorithm's greatest strength is the development of a CNN model that can be utilized for COVID viral prognosis using x-ray images to supplement existing COVID pandemic testing techniques. The proposed approach is tested using the metrics of training time, accuracy, precision, recall, and F1-score on commonly used benchmark image classification datasets. According to the experimental data, the CNN model with an image dimension of 55 x 55 surpasses the other CNN models in terms of training time, accuracy, recall, and F1-score. Several issues were raised about how to choose the best CNN models for prognostic picture categorization. [ FROM AUTHOR] Copyright of International Journal of Multidisciplinary: Applied Business & Education Research is the property of Future Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Automatic classification Infectious disease X-ray images based on Deep learning Algorithms

Recent technological advancements allow deep learning to be employed in practically every aspect of life. Because deep learning techniques are so precise, they can be used in medicine to classify and detect various diseases. The coronavirus (SARSCoV2) epidemic has recently affected global health systems. One of the ways to diagnose SARSCoV2 is with a chest X-ray. This paper proposes a deep learning technique to distinguish SARSCoV2 positive and normal cases. In this study, we fine-Tuned the deep learning models and hyperparameters, and the fine-Tuned deep learning models performed significantly better. To classify X-ray images, we developed a system based on deep learning algorithms that includes five models: Xception, VGG19, ResNet50, DenseNet121, and Inception. We offer deep learning models and algorithms that have been trained and evaluated to support medical efforts and reduce medical staff workload when dealing with SARSCoV2. In addition, the classification model that was proposed yields positive results because it makes use of accurate classification of the SARSCoV2 disease based on medical images. Additionally, the performance of our proposed CNN classification method for medical imaging was evaluated using various edge-based neural networks. The accuracy of tertiary classification with CNN will decrease as the number of classes in the training network grows. In tertiary classification, which includes normal and SARSCoV2 positive images, the proposed model achieved a 0.9897 accuracy. The proposed algorithm achieves a high level of classification accuracy when using the DenseNet121 model for binary classification. © 2022 IEEE.

Diagnosing COVID-19 in Chest X-ray Images based on Deep Learning: Transfer Learning versus Deep Features Extraction

Chest X-ray (CXR) images provide an effective modality for detecting COVID-19 infections. Nevertheless, the interpretation of CXR images is challenging and operator-dependent task. Several studies proposed the use of pretrained convolutional neural network (CNN) models to classify CXR images with the goal of detecting COVID-19 infections. In fact, the classification of CXR images using the pretrained CNN models is essentially performed using two approaches, namely the transfer learning approach and deep features extraction approach. This study aims to compare the performance of these two approaches to classify CXR images as COVID-19, pneumonia, and normal. Three pretrained CNN models, namely the AlexNet, VGG19, and ResNet50 CNN models, have been utilized. Furthermore, a balanced dataset of CXR images is used to perform the analysis, where this dataset includes 1,228 COVID-19 CXR images, 1,228 pneumonia CXR images, and 1,228 normal CXR images. For the three pretraiend CNN models, the deep features extraction approach achieved better classification results compared with the transfer learning approach. Moreover, the results show that the ResNet50 CNN model obtained the highest classification performance based on the transfer learning approach and the deep features extraction approach. The highest macro-averaged sensitivity, specificity, and F1 score values, which have been achieved using the deep features extraction approach and the ResNet50 CNN model, are equal to 93.7%, 96.9%, and 93.7%, respectively. © 2022 IEEE.

CXGNet: A tri-phase chest X-ray image classification for COVID-19 diagnosis using deep CNN with enhanced grey-wolf optimizer.

The coronavirus disease 2019 (COVID-19) epidemic had a significant impact on daily life in many nations and global public health. COVID's quick spread has become one of the biggest disruptive calamities in the world. In the fight against COVID-19, it's critical to keep a close eye on the initial stage of infection in patients. Furthermore, early COVID-19 discovery by precise diagnosis, especially in patients with no evident symptoms, may reduce the patient's death rate and can stop the spread of COVID-19. When compared to CT images, chest X-ray (CXR) images are now widely employed for COVID-19 diagnosis since CXR images contain more robust features of the lung. Furthermore, radiologists can easily diagnose CXR images because of its operating speed and low cost, and it is promising for emergency situations and therapy. This work proposes a tri-stage CXR image based COVID-19 classification model using deep learning convolutional neural networks (DLCNN) with an optimal feature selection technique named as enhanced grey-wolf optimizer with genetic algorithm (EGWO-GA), which is denoted as CXGNet. The proposed CXGNet is implemented as multiple classes, such as 4-class, 3-class, and 2-class models based on the diseases. Extensive simulation outcome discloses the superiority of the proposed CXGNet model with enhanced classification accuracy of 94.00% for the 4-class model, 97.05% of accuracy for the 3-class model, and 100% accuracy for the 2-class model as compared to conventional methods.

Convolutional Neural Network Techniques on X-ray Images for Covid-19 Classification

At the end of 2019, the World Health Organization (WHO) referred that the Public Health Commission of Hubei Province, China, reported cases of severe and unknown pneumonia. A new coronavirus, SARS-CoV-2, was identified as responsible for the lung infection, called COVID-19 (coronavirus disease 2019). An early diagnosis of those carrying the virus becomes crucial to contain the spread, morbidity and mortality of the pandemic. The definitive diagnosis is made through specific tests, among which imaging tests play a very important role. Achieving this goal cannot be separated from radiological examination, and chest X-ray is the most easily available and least expensive alternative. The use of X-ray chest radiographs, as an element that assists the diagnosis and that allows the follow up of the disease, is the subject of many publications that adopt machine learning approaches. This work focuses on the most adopted Convolutional Neural Network Techniques applied on chest X-ray images. © 2021 IEEE.

Classification of chest X-ray images using a hybrid deep learning method

This work presents a technique for classifying X-ray images of the chest (CXR) by applying deep learning-based techniques. The CXR will be classified into three different types, i.e. (i) normal, (ii) COVID-19, and (iii) pneumonia. The classification challenge is raised when the X-ray images of COVID-19 and pneumonia are subtle. The CXR images of the chest are first proceeded to be standardized and to improve the visual contrast of the images. Then, the classification is performed by applying a deep learning-based technique that binds two deep learning network architectures, i.e., convolution neural network (CNN) and long short-term memory (LSTM), to generate a hybrid model for the classification problem. The deep features of the images are extracted by CNN before the final classification is performed using LSTM. In addition to the hybrid models, this work explores the validity of image pre-processing methods that improve the quality of the images before the classification is performed. The experiments were conducted on a public image dataset. The experimental results demonstrate that the proposed technique provides promising results and is superior to the baseline techniques. © 2022 Institute of Advanced Engineering and Science. All rights reserved.