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.

Identification of COVID-19 in CT Images using Deep Learning Model

Worldwide, COVID-19 has had a substantial impact on patients and hospital systems. Early identification and diagnosis are essential for regulating the growth of COVID-19. The input CT screening images are initially segmented into various regions using the Fuzzy C-means (FCM) clustering technique. Next, region-based image quality enhancement employs a histogram equalization method. Furthermore, certain necessary data is represented in a new image using the Local Directional Number technique. Lastly, the input images are portioned with the help of a traditional convolutional neural network model. The proposed convolutional neural network based system was able to give an accuracy of 98.60%, and the results revealed that methods for detecting COVID-19 impact from CT scan images must be developed significantly before considering it as a medical choice. Moreover, many diverse datasets are essential to assess the processes in a real-world setting. © 2022 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.

Face Mask Detection Using Viola-Jones and Cascade Classifier

Globally, the COVID-19 coronavirus outbreak is causing chaos in human health and therefore, the healthcare sector is in serious disarray. Many precautions have been taken to prevent the spread of this disease, including the usage of masks, which is strongly recommended by the World Health Organization (WHO). This research study has used the Viola-Jones algorithm for detecting face masks, where Histogram Equalization, Unsharp Filter and Gamma Correction are used as the preferred image pre-processing techniques to improve the overall accuracy. Haar Feature Selection is applied for creating integral images and AdaBoost training is performed on these images. Cascade classifier, a machine learning-based approach, is also integrated with the base algorithm where a cascade function assists Viola-Jones in accurately detecting objects in images. A total number of 1670 images is used in this work and our system is compared with four other machine learning algorithms, where Viola-Jones outperforms these ML-based classifiers and the overall accuracy obtained is 96%. © 2022 IEEE.

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

Preprocessing Methods Study to Improve Information Technology for Person Identification by Occluded Image

The coronavirus pandemic has become challenging issue for the face recognition and identification technologies. Most algorithms failed because of the presence of medical masks on the faces. Such an issue made it difficult for the decision-making systems to provide the correct results during the face recognition and person identification process. Although, for the past three years many of these problems have been overcome, the new adversary attacks arose, that allow to evade the identification systems. Therefore, the development of information technologies for person identification robust to the presence of occlusion on faces is still up to date. This paper describes the preprocessing methods study with an aim to improve performance of information technology of person identification by occluded face image. Information technology is based on the algorithm that consist of Gabor wavelet transformation as an image processing method for forming a global face image, local binary patterns in one-dimensional space and a histogram of oriented gradients for forming a vector of image features, Euclidean squared distance metric for vector classification. For the purpose of information technology improvement, the experimental research was conducted with the use of variety of preprocessing methods: anisotropic diffusion, image histogram equalization and both of these methods applied. During the research there were used The Database of Faces database, the FERET database and the SCface database. Images from these databases were processed in order to consider it occluded and converted to uncompressed and compressed formats to conduct the experiments more clearly. The results of the experiments have shown that preprocessing by anisotropic diffusion and image histogram equalization along with conversion to uncompressed format can increase the accuracy of the algorithm performance on 5-7.5% in some cases. Also, the usage of image histogram equalization by itself on the images converted to compressed format can increase the identification accuracy rate of the algorithm on 2.5%. © 2022 Copyright for this paper by its authors.

Diagnosis and Prediction of using Machine Learning Algorithm for CT Images

COVID-19 is associated with a high mortality rate all over the world. Early detection and prevention of COVID-19 delivers better protection. This procedure employs Histogram Equalization for image preprocessing as well as feature extraction, as well as a Gradient Boosting Algorithm (GBA) classifier to determine whether a patient's condition is normal or abnormal. The classifier's performance is determined by the number of correct and incorrect classifications. Early detection and treatment of covid-19 can significantly improve the survival rate of patients with computed Tomography (CT) images of the lungs that use mathematical morphological operations. The median filter removes speckle noise from images while improving contrast. Here, active contour processing is used to locate corona virus location. © 2022 IEEE.

COVID-19 Detection in CXR Image Using High Frequency Emphasis Filtering Based Convolutional Neural Network

As a huge disaster for humanity, the COVID-19 has caused many negative effects on the lives of people around the world with a rapid growth. Moreover, the global pandemic of Neocoronavirushas produced many mutated strains. Although the most commonly used test for COVID-19 is reverse transcription-polymerase chain reaction (RT-PCR), CXR becomes an irreplaceable tool for the diagnosis and analysis for a more complete and accurate visualization of the lung lesion process. Therefore, it is of high value for classification and identification studies. In this paper, the high-frequency emphasis filtering based convolutional neural networks (HFEF-CNN) are proposed for solving the automatic detection of COVID-19. Firstly, the HFEF is used to denoise the image data to make some features in the image more obvious. Then some major CNNs are used to train image classification models to achieve better detection performance. Finally, Some experiments are conducted on the 'COVID-19 Chest X-Ray Database' dataset. To verify the effectiveness of the HFEF-CNN, a histogram equalization based CNN (HE-CNN) and a restricted contrast adaptive histogram equalization based CNN (CLAHE-CNN) are compared. The experimental results show that the HFEF-CNN outperformed the above two methods. © 2022 IEEE.

Contrast Enhancement and Noise Reduction of chest X-ray images

X-ray radiography is used to get medical images of body parts such as chest, bones etc. These images help in detection of anomaly in inspected body part, for eg- Chest X-ray are used for detection of many diseases such as Covid-19, Pneumonia and Cancer. However, images obtained from radiography are low in contrast and with high noise level. Enhancement of an image is very crucial for the diagnostic purpose, as currently medical images are very helpful in identifying various disease and problem in human body. With the technical support, the enhancement is considered one of the first-rate methods for the betterment of visualization and raising the standard for understanding and clearing the image details. In our work, we have focused on the contrast enhancement and noise reduction, using Histogram equalization, CLAHE (Contrast Limited Adaptive Histogram Equalization), median filter and DCT filter for chest X-ray images of COVID-19 positive patients. The dataset of 6,334 images are collected from the Kaggle repository. All these methods are combined and as a result, has provided the best output by giving a colored enhanced image, highlighting the major details. This work will be helpful in the diagnosis of various kind of the diseases from radiographic approach. In the future, we will extend the process for the diagnostic part of COVID-19 from the enhanced images dataset, which will help in easy detection and work as a technological support to healthcare system. © 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.

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.

CNN-based diagnosis of COVID-19 Pneumonia: A comparative study on different image preprocessing methods

CT image diagnosis of COVID-19, an infectious disease that causes respiratory problems, proved efficient with CNN-based methods. The accuracy of these machine learning methods relies on the quality and dispersion of the training set, which has often been ensured by utilizing the preprocessing strategies. However, few studies investigated the impact of different preprocessing methods on accuracy rates in diagnosing COVID-19. As a result, a comparative study on different image preprocessing methods was done in this work. Two popular preprocessing methods contrast limited adaptive histogram equalization (CLAHE) and Discrete Cosine Transform (DCT), which were processed and compared in a CNN-based diagnosis framework. With a mixed and open-source dataset, the experimental results showed that DCT based preprocessing method had a higher accuracy on the test set, which was 92.71%. © 2021 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.

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