Chest X-ray Imaging System for Early Detection of Tuberculosis

Today it is observed that few people respect the biosecurity measures announced by the WHO, which aimed to reduce the amount of COVID-19 infection among people, even knowing that this virus has not disappeared from our environment, being an unprecedented infection in the world. It should be noted that before this pandemic, tuberculosis affected millions of people, having a great role because it is highly contagious and directly affects the lungs, although it has a cure, if it is not treated in time it can be fatal for the person, although there are many methods of detection of tuberculosis, one that is most often used is the diagnosis by chest x-ray, although it has low specificity, when the image processing technique is applied, tuberculosis would be accurately detected. In view of this problem, in this article a chest X-ray image processing system was conducted for the early detection of tuberculosis, helping doctors to detect tuberculosis accurately and quickly by having a second opinion by the system in the analysis of the chest x-ray, prevents fatal infections in patients. Through the development of the tuberculosis early detection system, it was possible to observe the correct functioning of the system with an efficiency of 97.84% in the detection of tuberculosis, detailing the characteristics presented by normal or abnormal images so that the doctor detects tuberculosis in the patient early. © 2023 IEEE.

A DEEP LEARNING APPROACH LUNG SEGMENTATION AND PNEUMONIA DETECTION FROM X-RAYS

Lung segmentation is a process of detection and identification of lung cancer and pneumonia with the help of image processing techniques. Deep learning algorithms can be incorporated to build the computer-aided diagnosis (CAD) system for detecting or recognizing broad objects like acute respiratory distress syndrome (ARDS), Tuberculosis, Pneumonia, Lung cancer, Covid, and several other respiratory diseases. This paper presents pneumonia detection from lung segmentation using deep learning methods on chest radiography. Chest X-ray is the most useful technique among other existing techniques, due to its lesser cost. The main drawback of a chest x-ray is that it cannot detect all problems in the chest. Thus, implementing convolutional neural networks (CNN) to perform lung segmentation and to obtain correct results. The 'lost' regions of the lungs are reconstructed by an automatic segmentation method from raw images of chest X-ray. © 2022 IEEE.

Machine Learning for Diabetes, Heart Disease, Pneumonia, and Covid-19 Diagnostics

Machine Learning, a part of artificial intelligence which is applied in numerous health-related sector which includes the development of innovative medical procedures, the treatment of chronic diseases and the management of medical data. If a patient can recognize the disease at an early stage from the ease of home, they can start their medication sooner and consult a doctor accordingly for their treatment. This paper attempts to detect various diseases in the healthcare field such as Covid-19 and Pneumonia using Image processing technique with the help of Convolutional Neural Network, and other diseases such as Heart Disease and Diabetes using Random Forest, XGBoost, Support Vector Machine and K-Nearest Neighbour Classifiers. © 2022 IEEE.

The One-stop Intelligent Environmental Protection Community based on Image Processing Techniques

In view of the spread of COVID-19 epidemic and many problems existing in the community, such as potential safety hazards, diluted interpersonal relationships, and out-of-place management, a system of one-stop intelligent environmental protection communities based on the Internet was proposed. It not only improves the ability of community staff, provides great convenience for residents and community workers, cares for vulnerable groups, and promotes a happy and harmonious neighborhood life, but also scores residents and staff while monitoring the community environment for safety with the technology application of thermal imaging recognition, PaddleHub-based face and mask recognition. This new system design is easy to implement at low cost and has a simple structure with many functions. The technologies for face and mask recognition proposed in this paper are based on PaddleHub. Experiments on MaskedFace-Net provided by Haute-Alsace University and the pretrained parameters loaded by PaddleHub showed that the accuracy rate with mask recognition was 94.3290 percent using this method. © 2022 ACM.

Genomic image representation of human coronavirus sequences for COVID-19 detection

Coronavirus (CoV) disease 2019 (COVID-19) is a severe pandemic affecting millions worldwide. Due to its rapid evolution, researchers have been working on developing diagnostic approaches to suppress its spread. This study presents an effective automated approach based on genomic image processing (GIP) techniques to rapidly detect COVID-19, among other human CoV diseases, with high acceptable accuracy. The GIP technique was applied as follows: first, genomic graphical mapping techniques were used to convert the genome sequences into genomic grayscale images. The frequency chaos game representation (FCGR) and single gray-level representation (SGLR) techniques were used in this investigation. Then, several statistical features were obtained from the images to train and test many classifiers, including the k-nearest neighbors (KNN). This study aimed to determine the efficacy of the FCGR (with different orders) and SGLR images for accurately detecting COVID-19, using a dataset containing both partial and complete genome sequences. The results recommended the fourth-order FCGR image as a proper genomic image for extracting statistical features and achieving accurate classification. Furthermore, the results showed that KNN achieved an overall accuracy of 99.39% in detecting COVID-19, among other human CoV diseases, with 99.48% precision, 99.31% sensitivity, 99.47% specificity, 0.99 F1-score, and 0.99 Matthew's correlation coefficient. © 2022 THE AUTHORS

Smart Crowd Monitoring System using Image Processing Technique During Covid-19

During the COVID-19 scenario, due to partial lockdown, people did not have permission to go out and buy items freely. Instead, they were given a specific time for purchasing goods. As a result, people were found in multitudes during these hours, without maintaining social distance. Managing this crowd to maintain social distance is a huge task for the government, and hence a system that will assist them in controlling the people is required. The You Only Look Once (YOLO) approach was used to detect the objects. Compared to other object detection methods, this technique has a lot of advantages. YOLO finds objects by applying convolutional networks to forecast bounding boxes and class probabilities for these boxes, and it does it much faster than the existing works. This paper develops a device using a Raspberry Pi-4 board that detects people who are in the frame of the camera, and if they are closer than the distance allocated in the device, an alarm will sound, informing them that they are breaking the rules, and the alert message will be sent to the nearby police station. In this way, the crowd can be managed in a pandemic situation. © 2022 IEEE.

Deep Learning Approach for Designing and Development of Risk Level Indicator for Patients with Lung Diseases

"Lung disease"as a medical term, discusses as several disorders that affects both lungs. There are different types of lung disease like Asthma, lungs infections like Influenza, Pneumonia, Tuberculosis, and numerous other types of breathing problems including Lung cancers. These lung diseases can be the main reason for failure in breathing. Due to COVID19 pandemic, Pneumonia and COVID19 were highlighted mostly as fatal diseases if not detected on time. Newly identified COVID19 diseases has caused many deaths and confirmed detections reported worldwide, followed with a greatest risk to community wellbeing, especially for patients with lung diseases. Process of developing a clinically accepted vaccine or specific therapeutic drug for this disease are not finalized, which will contribute to the expansion of actual prevention action plans. Thus, methods to detect lung illness accurately and efficiently is important. Proposed solution will easily and precisely detect the risk level of patients with these two lung diseases Pneumonia and COVID19 using a mobile application with chest radiography (Chest X-rays), which is considered as a cheap, easy to access and speedy manner. Proposed solution will identify, classify and evaluate the risk level of the patient suffering with the use of Image Processing, Machine Learning techniques and Convolutional Neural Networks. So, anybody who use the proposed solution may have the ability to have a precious decision about own medical condition accurately, quickly with low cost. Proposed solution can calculate severity level of a patient with more than 97% accuracy with chest radiography analysis together with patient's current symptoms and breath holding time evaluation. © 2022 IEEE.

Impact and Investigation of nCOV 2 in the Indian State of Uttarakhand

In this research work, we have presented a brief study of the impact of the Novel Coronavirus in my hometown Dehradun, in the State of Uttarakhand, India. Here we discussed the impact of Coronovirus on various sectors and districts of the State. Here we have also discussed the State government’s steps and precautions to fight this global epidemic. We have also presented a change detection methodology to identify coronavirus’s impact on the patient’s chest using image processing techniques. Pre and post-DICOM images of Covid infected person are analyzed based on the statistical image parameters. Texture classification of the pre and post DICOM images is performed based on the visual statistical features, i.e., contrast, correlation, energy, and homogeneity. Finally, for both the images histogram signature plotting is performed, and based on this, changes developed in the DICOM images are monitored. © 2021 IEEE