Diagnosis of COVID-19 from the X-Ray images using BAT Algorithm with Deep Convolutional Neural Network

The COVID-19 widespread has posed a chief contest to the scientific community around the world. For patients with COVID-19 illness, the international community is working to uncover, implement, or invent new approaches for diagnosis and action. A opposite transcription-polymerase chain reaction is currently a reliable tactic for diagnosing infected people. This is a time- and money-consuming procedure. Consequently, the development of new methods is critical. Using X-ray images of the lungs, this research article developed three stages for detecting and diagnosing COVID-19 patients. The median filtering is used to remove the unwanted noised during pre-processing stage. Then, Otsu thresholding technique is used for segmenting the affected regions, where Spider Monkey Optimization (SMO) is used to select the optimal threshold. Finally, the optimized Deep Convolutional Neural Network (DCNN) is used for final classification. The benchmark COVID dataset and balanced COVIDcxr dataset are used to test projected model's performance in this study. Classification of the results shows that the optimized DCNN architecture outperforms the other pre-trained techniques with an accuracy of 95.69% and a specificity of 96.24% and sensitivity of 94.76%. To identify infected lung tissue in images, here SMO-Otsu thresholding technique is used during the segmentation stage and achieved 95.60% of sensitivity and 95.8% of specificity. © 2023 IEEE.

Deep Learning for Crowd Image Classification for Images Captured Under Varying Climatic and Lighting Condition

Most of recent events have attracted a lot of attention towards importance of automatic crowd classification and management. COVID-19 is the most setback for the entire world. During these events proper breakout and public crowd management leads to the requirement of managing, counting, securing as well as tracking the crowd. But automatic analysis of the crowd is very challenging task because of varying climatic and lighting conditions, varying postures etc. During this paper we have developed PYTHON based system for automatic crowd images classification using Deep learning. This paper is the first attempt for automatic classification of crowd images. We have prepared the dataset of crowd classification consisting of three categories. The proposed methodology of crowd classification starts with preprocessing during which we have used median filtering for noise removal. Deep learning models are developed using 70% training images. The performance of the system is evaluated for various deep learning algorithms including one block VGG, two block VGG and three block VGG. We have also evaluated the performance of three block VGG using dropout. VGG16 transfer learning based crowd classification is developed using PYTHON. Using VGG16 transfer learning we achieved the accuracy of 69.44.% which is highest among all deep learning classification models during this study © 2022 IEEE.

Infrared Thermal Imaging Face Recognition Method Based on Temperature Block Feature

In order to prevent COVID-19 effectively, non-contact body temperature measurement and human identification are required in public places, but face recognition based on visible light cannot meet the requirements. Therefore, this paper proposes a thermal imaging face recognition method based on temperature block feature extraction. Histogram equalization and median filter are used to preprocess the face image, and Sobel operator is used for face detection;Six dimensional features including temperature mean, standard deviation and adjacent difference are extracted from each temperature block in the average poolinged temperature matrix, and classified by max-correlation-coefficient method. The experimental results show that the recognition rate of this method is 6.1% higher than that of PCA method with the temperature block size of boldsymbol{2times 2}. When using the same hardware to execute the program, if the two recognition rates are very close, the average test time of the proposed method is 22.2% less than the one of deep learning models such as Alexnet. Furthermore, the proposed method has strong robustness for small training sample set. For example, the recognition rate of single training sample model can reach 0.7, while in the deep learning model, except Mobilenet can reach 0.6, all of the others are less than 0.4. © 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.

Comparative Analysis of Image Enhancement Techniques for Chest X-ray Images

X-ray radiography plays a crucial part in diagnosis of various diseases in human body like Covid-19, Cancer and Pneumonia. The images obtained through X-ray radiography is interpreted by Surgeons, Pathologists and Radiologists for detecting anomaly in scanned body part. Chest X-ray is one of the cheapest and easily accessible tests of functioning of chest and lungs. However, images obtained through X-ray are not very clear, low in contrast and with lesser variation in gray level. Image enhancement is done for better visualization of images and bringing forward the underlying details of image. The Kaggle repository of total 6334 chest X-ray images were used for experimentation and calculation works. In this paper, we have compared various combinations of contrast enhancement techniques such as CLAHE, Morphological operations (black and white hat transforms) and noise reduction techniques like Median filter, DCT and DWT. The Comparison was done on the basis of image quality assessment parameters such as MSE, PSNR, and AMBE. The results showed that fusion of CLAHE and DWT techniques gave best results with highest PSNR value and lowest AMBE among the various models discussed. The proposed methodology shall be very helpful in diagnosis of diseases from chest X-ray images. © 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 Hybrid MSVM COVID-19 Image Classification Enhanced with Swarm Feature Optimization

COVID-19 (novel coronavirus disease) is a serious illness that has killed millions of civilians and affected millions around the world. Therefore, numerous technologies that enable both the rapid and accurate detection of COVID-19 illnesses will provide much assistance to healthcare practitioners. A machine learning-based approach is used for the identification of COVID-19. In general, artificial intelligence (AI) approaches have yielded positive outcomes in healthcare visual processing and analysis. CXR is the digital image processing method that plays a significant role in the analysis of corona disease. In this research article, at the initial phase of the process, a median filter is used for the noise reduction from the image. Edge detection is an essential step in the process of COVID-19 detection. The canny edge detector is implemented for the detection of edges in the CXR images. The principal component analysis (PCA) method is implemented for the feature extraction phase. There are multiple features extracted through PCA. The essential features are optimized by an optimization technique known as swarm optimization is used for feature optimization. For the recognition of COVID-19 through CXR images, a hybrid multi-class support vector machine technique is implemented. The particle swarm optimization (PSO) technique is used for feature optimization. The proposed system has achieved an accuracy of 97.51%, specificity (SP) of 97.49%, and 98.0% of sensitivity (SN). © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Covid-19 Classification Using HOG-SVM and Deep Learning Models

COVID-19 is measured as the biggest hazardous and fast infectious grief for the human body which has a severe impact on lives, health, and the community all over the world. It is still spreading throughout the world with different variants which is silently killing many lives globally. Thus, earlier diagnosis and accurate detection of COVID-19 cases are essential to protect global lives. Diagnosis COVID-19 through chest X-ray images is one of the best solutions to detect the virus in the infected person properly and quickly at a low cost. Encouraged by the existing research, in this paper, we proposed a hybrid model to classify the Covid cases and non-Covid cases with chest X-ray images based on feature extraction, machine learning and deep learning techniques. Two feature extractors, Histogram Oriented Gradient (HOG) and CNN (MobileNetV2, Sequential, ResNet152V2) are used to train the model. For the classification, we utilized two approaches: Support Vector Machine (SVM) for machine learning and CNN (MobileNetV2, Sequential, ResNet152V2) classifiers for deep learning. The experimental result analysis shows that the Sequential model and the ResNet152V2 model achieve 100% and 82.6% accuracy respectively which is satisfactory. On the other hand, the HOG-SVM method successfully detects all the test images correctly which provides the best result with 100% accuracy, specificity, and responsiveness over a limited public dataset. © 2022 IEEE.

Optimal Hybrid Feature Extraction with Deep Learning for COVID-19 Classifications

Novel coronavirus 2019 (COVID-19) has affected the people's health, their lifestyle and economical status across the globe. The application of advanced Artificial Intelligence (AI) methods in combination with radiological imaging is useful in accurate detection of the disease. It also assists the physicians to take care of remote villages too. The current research paper proposes a novel automated COVID-19 analysis method with the help of Optimal Hybrid Feature Extraction (OHFE) and Optimal Deep Neural Network (ODNN) called OHFE-ODNN from chest x-ray images. The objective of the presented technique is for performing binary and multi-class classification of COVID-19 analysis from chest X-ray image. The presented OHFE-ODNN method includes a sequence of procedures such as Median Filtering (MF)based pre-processed, feature extraction and finally, binary (COVID/Non-COVID) and multiclass (Normal, COVID, SARS) classification. Besides, in OHFE-based feature extraction, Gray Level Co-occurrence Matrix (GLCM) and Histogram of Gradients (HOG) are integrated together. The presented OHFE-ODNN model includes Squirrel Search Algorithm (SSA) for fine-tuning the parameters of DNN. The performance of the presented OHFE-ODNN technique is conducted using chest x-rays dataset. The presented OHFE-ODNN method classified the binary classes effectively with a maximum precision of 95.82%, accuracy of 94.01% and F-score of 96.61%. Besides, multiple classes were classified proficiently by OHFE-ODNN model with a precision of 95.63%, accuracy of 95.60% and an F-score of 95.73%. © 2022 Tech Science Press. All rights reserved.