COVID-LiteNet: A lightweight CNN based network for COVID-19 detection using X-ray images

To restrict the virus's transmission in the pandemic and lessen the strain on the healthcare industry, computer-assisted diagnostics for the accurate and speedy diagnosis of coronavirus illness (COVID-19) has become a prerequisite. Compared to other types of imaging and detection, chest X-ray imaging (CXR) provides several advantages. Healthcare practitioners may profit from any technology instrument providing quick and accurate COVID-19 infection detection. COVID-LiteNet is a technique suggested in this paper that combines white balance with Contrast Limited Adaptive Histogram Equalization (CLAHE) and a convolutional neural network (CNN). White balance is employed as an image pre-processing step in this approach, followed by CLAHE, to improve the visibility of CXR images, and CNN is trained using sparse categorical cross-entropy for image classification tasks and gives the smaller parameters file size, i.e., 2.24 MB. The suggested COVID-LiteNet technique produced better results than vanilla CNN with no pre-processing. The proposed approach outperformed several state-of-the-art methods with a binary classification accuracy of 98.44 percent and a multi-class classification accuracy of 97.50 percent. COVID-LiteNet, the suggested technique, outperformed the competition on various performance parameters. COVID-LiteNet may help radiologists discover COVID-19 patients from CXR pictures by providing thorough model interpretations, cutting diagnostic time significantly. © 2023 IEEE.

Study of Deep Learning Approaches for Diagnosing Covid-19 Disease using Chest CT Images

Since March 2020, the World Health Organization (WHO) has declared COVID-19 a pandemic. An evolving viral infection with respiratory tropism causes atypical pneumonia. Experts believe that detecting COVID-19 early stage is crucial. Early diagnosis and tracking techniques have become increasingly important to ensure an accelerated treatment process and avoid virus spread. Images from Computed Tomography (CT) scans can provide quick and precise COVID-19 screening. A subdivision of Machine Learning (ML) called Deep Learning (DL) can improve diagnostic accuracy and speed by automating screening via medical imaging in collaborative efforts with radiologists and physicians This study aims to investigate the recently popularized and extensively discussed deep learning algorithms for COVID-19 diagnosis in connection to the sequence phases involved in image processing. Getting rid of the noise in these images requires some preprocessing. Histogram equalization, fuzzy histogram equalisation, Adaptive Histogram Equalization (AHE) and Contrast Limited Adaptive Histogram Equalization (CLAHE) are used to improve the image quality and therefore increase the identification of the image. Afterwards, necessary features for disease detection are segmented using various deep models like U-Net, U-Net + FPN (Feature Pyramid Network), COVID-SegNet and Dense GAN. Once these distinct deep characteristics have been identified, they are extracted using a variety of different deep models. Finally, an illness is diagnosed using popular models such as SVM, ResNet-50, AlexNet, VGG16, DenseNet, and SqueezeNet. The deep learning models with a better optimization algorithm to be effective in the diagnosis of COVID-19 and also obtain a reduced and efficient feature set for image classification and feature extraction. © 2023 IEEE.

Diagnosis of Pulmonary Diseases from Chest X-ray Using Deep Learning Approaches

In recent years, a severe pandemic has struck worldwide with the utmost shutter, enforcing a lot of stress in the medical industry. Moreover, the increasing population has brought to light that the work bestowed upon the healthcare specialists needs to be reduced. Medical images like chest X-rays are of utmost importance for the diagnosis of diseases such as pneumonia, COVID-19, thorax, and many more. Various manual image analysis techniques are time-consuming and not always efficient. Deep learning models for neural networks are capable of finding hidden patterns, assisting the experts in specified fields. Therefore, collaborating these medical images with deep learning techniques has paved the path for enormous applications leading to the reduction of pressure embarked upon the health industry. This paper demonstrates an approach for automatic lung diagnosing of COVID-19 (coronavirus) and thorax diseases from given CXR images, using deep learning techniques. The previously proposed model uses the concept of ResNet-18, ResNet-50, and Xception algorithms. This model gives the highest accuracy of 98% without segmentation and 95% with segmentation. Whereas, the proposed model uses CNN and CLAHE algorithms which achieves an accuracy of 99.22% without segmentation and 98.39% with segmentation. Therefore, this model will be able to provide assistance to health workforces and minimize manual errors precisely. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Chest X ray image enhancement using deep contrast diffusion learning

The chest x-ray (CXR) is a diagnostic imaging tool that aids in the early detection and diagnosis of lung abnormalities. Due to scattering radiation, the CXR would have poor contrast, and the diagnosis would be difficult. Although many methods exist, deep learning (DL)-based CXR image enhancement remains difficult due to the amount of contrast that needs to be enhanced and the locations where acceptable contrast must be extracted. In order to improve CXR images, a contrast diffusion network is introduced in this paper. The input image is initially placed through a multi-level contrast-limited adaptive histogram Equalization (CLAHE) process, from which the necessary contrast is extracted and sent into the convolutional neural network (CNN)-based residual learning network together with low contrast CXR. To create the enhanced CXR images, the learned contrast features were diffused over the input image. The amount of contrast to be diffused is determined by multiple levels of CLAHE. Various metrics are used to evaluate the enhanced image's quality. Additionally, the enhanced images are submitted to computer-assisted diagnosis, which improves overall classification efficiency. All of the results are based on the Shenzhen, COVID-CXR, and PadChest datasets. © 2023 Elsevier GmbH

Lung disease detection using Self-Attention Generative Adversarial Capsule network optimized with sun flower Optimization Algorithm

Lung cancer is the uncontrolled growth of abnormal cells in one or both lungs. This is one of the dangerous diseases. A lot of feature extraction with classification methods were discussed previously regarding this disease, but none of the methods give sufficient results, not only that, those methods have high over fitting problem, as a result, the detection accuracy was minimizing. Therefore, to overcome these issues, a Lung Disease Detection using Self-Attention Generative Adversarial Capsule Network optimized with Sun flower Optimization Algorithm (SA-Caps GAN-SFOA-LDC) is proposed in this manuscript. Initially, NIH chest X-ray image dataset is gathered through Kaggle repository to diagnose the lung disease. Then, the chests X-ray images are pre-processed by using the contrast limited adaptive histogram equalization (CLAHE) filtering method to eliminate the noise and to enhance the image quality. These pre-processed outputs are fed to feature extraction process. In the feature extraction process, the empirical wavelet transform method is used. These extracted features are given into Self-Attention based Generative Adversarial Capsule classifier for detecting the lung disease. The hyper parameters of SA-Caps GAN classifier is optimized using Sun flower Optimization Algorithm. The simulation is implemented in MATLAB. The proposed SA-Caps GAN-SFOA-LDC method attains higher accuracy 21.05%, 33.28%, 30.27%, 29.68%, 32.57% and 44.28%, Higher Precision 30.24%, 35.68%, 32.08%, 41.27%, 28.57% and 34.20%, Higher F-Score 32.05%, 31.05%, 36.24%, 30.27%, 37.59% and 22.05% analyzed with the existing methods, SVM-SMO-LDC, CNN-MOSHO-LDC, XGboost-PSO-LDC respectively. © 2022 Elsevier Ltd

Efficient Grad-Cam-Based Model for COVID-19 Classification and Detection

Corona Virus (COVID-19) is a novel virus that crossed an animal-human barrier and emerged in Wuhan, China. Until now it has affected more than 119 million people. Detection of COVID-19 is a critical task and due to a large number of patients, a shortage of doctors has occurred for its detection. In this paper, a model has been suggested that not only detects the COVID-19 using X-ray and CT-Scan images but also shows the affected areas. Three classes have been defined;COVID-19, normal, and Pneumonia for X-ray images. For CT-Scan images, 2 classes have been defined COVID-19 and non-COVID-19. For classification purposes, pre-trained models like ResNet50, VGG-16, and VGG19 have been used with some tuning. For detecting the affected areas Gradient-weighted Class Activation Mapping (GradCam) has been used. As the X-rays and ct images are taken at different intensities, so the contrast limited adaptive histogram equalization (CLAHE) has been applied to see the effect on the training of the models. As a result of these experiments, we achieved a maximum validation accuracy of 88.10% with a training accuracy of 88.48% for CT-Scan images using the ResNet50 model. While for X-ray images we achieved a maximum validation accuracy of 97.31% with a training accuracy of 95.64% using the VGG16 model. © 2023 CRL Publishing. All rights reserved.

Efficient Grad-Cam-Based Model for COVID-19 Classification and Detection

Corona Virus (COVID-19) is a novel virus that crossed an animal-human barrier and emerged in Wuhan, China. Until now it has affected more than 119 million people. Detection of COVID-19 is a critical task and due to a large number of patients, a shortage of doctors has occurred for its detection. In this paper, a model has been suggested that not only detects the COVID-19 using X-ray and CT-Scan images but also shows the affected areas. Three classes have been defined;COVID-19, normal, and Pneumonia for X-ray images. For CT-Scan images, 2 classes have been defined COVID-19 and non-COVID-19. For classification purposes, pre-trained models like ResNet50, VGG-16, and VGG19 have been used with some tuning. For detecting the affected areas Gradient-weighted Class Activation Mapping (GradCam) has been used. As the X-rays and ct images are taken at different intensities, so the contrast limited adaptive histogram equalization (CLAHE) has been applied to see the effect on the training of the models. As a result of these experiments, we achieved a maximum validation accuracy of 88.10% with a training accuracy of 88.48% for CT-Scan images using the ResNet50 model. While for X-ray images we achieved a maximum validation accuracy of 97.31% with a training accuracy of 95.64% using the VGG16 model. © 2023 CRL Publishing. All rights reserved.

Comparison of image enhancement techniques and CNN models for COVID-19 classification using chest x-rays images

This paper compares two image enhancement techniques with five convolutional neural network (CNN) models to classify Covid-19 chest x-ray images. a contrast limited adaptive histogram (CLAHE) and gamma correction which is method to improve image histogram are compared with the original chest x-ray image. We use five publicly available pre-trained CNN models to detect COVID-19: MobileNet, MobileNetV2, DenseNet169, DenseNet201, and ResNet50V2. Our procedure was validated using the COVID-19 radiography database, which is a freely accessible resource. MoblileNet with gamma correction is well-suited for COVIC-19 classification, achieving an accuracy score of 87.53 percent on the first epoch and 95.46 percent after training 100 epochs with the shortest computation time. © 2022 IEEE.

A new method for detecting COVID-19 combining deep learning with improved histogram equalization and median filter

The new coronavirus (COVID-2019) epidemic outbreak has devastating impacts on people's daily lives and public healthcare systems. The chest X-ray image is an effective tool for diagnosing new coronavirus diseases. This paper proposes a new method to identify the new coronavirus from chest X-ray images to assist radiologists in fast and accurate image reading. We first enhance the contrast of X-ray images by using adaptive histogram equalization and eliminating image noise by using a median filter. Then, the X-ray image is fed to a sophisticated deep neural network (FAC-DPN-SENet) proposed by us to train a classifier, which is used to classify an X-ray image as usual or COVID-2019 or other pneumonia. Applying our method to a standard dataset, we achieve a classification accuracy of 93%, which is significantly better performance than several other state-of-the-art models, such as ResNet and DenseNet. This shows that the proposed method can be used as an effective tool to detect COVID-2019. © 2021 IEEE.

Stacked Dark COVID-Net: A Multi-class Multi-label Classification Approach for Diagnosing COVID-19 Using Chest X-Ray Images

Most challenging yet, the need of the hour is accurate diagnosis of COVID-19, as the Coronavirus cases are increasing drastically day-by-day. Ceaseless efforts by the researchers and innovators have led to the development of several diagnostic models based on Deep Learning for effective diagnosis of COVID-19. However, the Deep Learning techniques that have been developed so far, fail to address major challenges such as overfitting, stability, computation overhead due to the usage of the massive volume of parameters and problems associated with the multi-class classification. Also in the medical perspective, researchers often suffer to identify the infinitesimal difference that exists in the radiographic images among the several lung diseases which makes the decision-making process difficult. Thus, to curb the crisis and to provide promising solutions & expertise for accurate diagnosis, this paper presents a novel lightweight multi-class multi-label COVID-19 detection model to assist physicians with greater ease to fight against this pandemic situation. Radiographic images are pre-processed using Contrast Limited Adaptive Histogram Equalization (CLAHE) and classified using novel Stacked Dark COVID-Net. The proposed model is validated using chest X-ray images and the results confirm the efficacy of the proposed model in terms of classification accuracy, sensitivity, specificity and stability. © 2022, Springer Nature Switzerland AG.

A Comparative Study of Image Enhancement Methods for Human Skin Image

A person's skin serves as a primary line of protection against harmful chemical exposure. During the Covid 19 out-break, customer-provider interactions on social media increased, leading to improvements of the intelligent system for accurate skin type analysis. However, optimizing image quality before further analysis is an important step for training and testing data. As a result, image enhancement technologies contribute to the improvement of image quality. In this paper, we presets a study of four image enhancement techniques for improving the image contrast and detail of facial skin images required for further skincare analysis and treatment. The techniques of image enhancement include Histogram Equalization (HE), Contrast Limited Adaptive Histogram Equalization (CLAHE), Brightness Preserving Dynamic Fuzzy Histogram Equalization (BPDFHE), and Min-Max Contrast Stretching. The experimental results demonstrate that the CLAHE technique delivers the highest quality of clarity and also facilitates further image processing. © 2022 IEEE.

Coronavirus Infection Segmentation Detection Using UNET Deep Learning Architecture

The high potency and spread of the coronavirus pandemic has rapidly swept over a global scale, causing a large number of deaths and devastation. Its mutants have exaggerated the situation further, which has become a serious concern and a challenge for scientists, especially medical practitioners, to devise some advanced remedial actions. This paper intends to address this by developing a model based on deep learning for segmenting the affected regions in the lungs using CT-scan images. We propose a novel segmentation model based on UNet using Xception-Net in the encoder stage to detect covid-19 infection in CT-scans with two main aspects. It combines the local residual connections in the decoder unit of UNet with the typical global residual connections that lead to better performance. Also, the encoder component uses a pre-trained state-of-the-art feature extraction model that helps the system converge to the optimal value precisely due to the pre-trained weights. We apply a contrast-limited version of the adaptive histogram equalization in the data preparation stage to make the frequency of image pixels uniformly distributed. This decreases the biasedness in the model towards specific sections of CT-scans images. Our proposed model outperformed some existing counterparts, including TV-UNet, Inf-Net, ED-CNN. © 2021 IEEE.

Diagnostic Decision Support for Medical Imaging and COVID-19 Image Classification on ARM Mali GPU

The abrupt rise in Coronavirus cases has led to shortage of rapid and highly sensitive reverse transcriptase polymerase chain reaction (RT-PCR) testing kits for the diagnosis of coronavirus disease 2019 (COVID-19). Radiologists have found X-ray images could be useful for diagnosis of COVID. In this work, Diagnostic Decision Support for Medical Imaging (DDSM)++ is introduced to detect the different abnormal conditions in lung including COVID. The scarcity of COVID dataset is handled by using various spatial transform augmentation techniques, such as power law transformation, Gaussian blur, and sharpening. Also, to get the benefit of inference accelerators, an android mobile application is developed which is quantized and optimized for ARM Mali GPU. The DDSM++ model is an extended version of DDSM model (inspired from Densenet-121), and the X-ray images are preprocessed with Contrast Limited Adaptive Histogram Equalization (CLAHE) to improve the quality of X-ray images. The COVID X-ray images are obtained from the open source and the proposed method has obtained almost 98.47% accuracy for COVID detection. Further, the model is quantized to FP-16 using TFLITE and is utilized to benchmark the inference acceleration on Edge devices with ARM Mali GPU. About 30% and 80% reduction in inference time was observed for FP-32 and FP-16 models when run on ARM Mali GPU. Post quantization, about 5% drop in accuracy is observed for COVID detection. © 2021 IEEE.

Comparative Analysis of CLAHE and AHE on Application of CNN Algorithm in the Detection of Covid-19 Patients

In 2021, Covid-19 is no longer a new threat for people in Indonesia and the world. The virus that has spread since December 2019 has created many transformation in many aspects for society. Various detection tools are emerged continuously to support government in overcoming the Covid-19 pandemic. Numerous cases that continue to grow in community, certainly, also requires detection tools with the best performance to handle this pandemic. In the field of informatics, many researchers use chest X-ray images to detect Covid-19, as a practitioner in informatics, authors attempt to apply several algorithms to get the best performance from Covid-19 detection. The objective of this study is to apply the Contrast Limited Adaptive Histogram Equalization (CLAHE) and Adaptive Histogram Equalization (AHE) algorithms in the detection of Covid-19 using Convolutional Neural Network (CNN) algorithm with VGG19 model. The dataset used in this study was a total of 1000 chest X-ray images and 1000 normal chest X-ray images obtained through Kaggle. The results of this study show that application of CLAHE has the highest accuracy of 99% for Covid-19 detection using VGG19. It is proved that the application of Histogram Equalization is able to improve the detection performance. © 2021 IEEE

X-ray Images Detection of COVID-19 Based on Deepwise Separable DenseNet

The Coronavirus Disease (COVID-19) is spreading worldwide. X-ray imaging plays an important role in the diagnosis of COVID-19. In order to help doctors diagnose COVID-19 effectively, we proposed a novel model (DS-DenseNet), which based on depth separable dense. By adding an improved depth separable convolution layer, we reduced the amount of parameters and make the model lighter. In the viral pneumonia, COVID-19 and normal lung, 2905 sets of chest X-ray images were collected, and the restricted contrast limited adaptive histogram equalization (CLAHE) algorithm was applied to preprocess the images and the preprocessed images were input into the model. Meanwhile, SDensenet, VGG16, Resnet18, Resnet34 and Densenet121 were introduced as baseline models. Compared with Resnet34, the sensitivity, accuracy and specificity of DS-Densenet are increased by 2.5%, 2.0% and 1.5% respectively;compared with SDensenet, the number of parameters is reduced by 44.0%, but the effect is not reduced. The experimental results show that the depth separable convolution can effectively reduce the model parameters, and the proposed DS-Densenet has a good classification effect, which has a certain significance for the auxiliary diagnosis of COVID-19. © 2021 IEEE.

Texture Analysis of Ultrasound Images to Differentiate Pneumonia and Covid-19

Lung ultrasound can potentially diagnose lung abnormalities such as pneumonia and covid-19, but it requires high experience. Covid-19, as a global pandemic, has similar common symptoms as pneumonia. The proper diagnosis of covid-19 and pneumonia necessitates clinicians' high expertise and skill to classify Covid-19 disease. This paper presents an approach to differentiate pneumonia and covid-19 based on texture analysis of ultrasound images. The proposed scheme is based on the Gray Level Co-occurrence Matrix (GLCM) features computing with Contrast Limited Adaptive Histogram Equalization (CLAHE) and gamma transformation for image enhancement. The results of the feature extraction analysis for lung ultrasound images suggest that differentiating pneumonia and Covid-19 is possible based on image texture features. © 2021 IEEE.