Detection of Pneumonia by Binary Image Classification Using Hybrid Neural Networks

Pneumonia is a more contagious virus with worldwide health implications. If positive cases are detected early enough, spread of the pandemic sickness can be slowed. Pneumonia illness estimation is useful for identifying patients who are at risk of developing health problems. So, the conventional method like PCR kits used to detect the covid patients lead to an increase in pneumonia cases as it failed to detect at the earliest. A polymerase chain reaction (PCR) test will be performed right away on the blood or sputum to quickly identify the DNA of the bacteria that cause pneumonia. With the help of CXR images, the pneumonia is diagnosed with a high accuracy rate utilizing the HNN (Hybrid Neural Network) method. Thus, isolating them at the earlier stage and preventing the spread of disease. © 2022 IEEE.

Comparitive Analysis on Neural Networks based on their performance in Pneumonia Detection

Pneumonia has been a concerning issue worldwide. This infectious disease has a higher mortality rate than Covid-19. More than two million individuals lost their lives in 2019 out of which almost 600,000 were infants less than 5 years of age. Globally, identification of the disease is done manually by radiologists, but this method is highly unreliable as its accuracy is not sufficiently good. With the evolution of computational resources, especially the computing power of GPUs, it has become possible to train very deep CNNs. This study involves a comparative analysis of neural networks for pneumonia recognition. The goal is to do binary image classification for pneumonia recognition on each of the three models, namely, a Sequential model using TensorFlow (built from scratch), ResNet50 and InceptionV3 and comparing their efficiency, to discover which model suits best for smaller datasets and which suits best for larger datasets. Dataset consists of 5856 anterior and posterior Chest X-Ray images labeled as either Normal or Pneumonic. © 2023 IEEE.

Analyzing Chest X-Ray to Detect the Evidence of Lung Abnormality Due to Infectious Disease

Analyzing chest X-ray is the must especially when are required to deal of infectious disease outbreak, and COVID-19. The COVID-19 pandemic has had a large effect on almost every facet of life. As COVID-19 was a disease only discovered in recent history, there is comparatively little data on the disease, how it is detected, and how it is cured. Deep learning is a powerful tool that can be used to learn to classify information in ways that humans might not be able to. This allows computers to learn on relatively little data and provide exceptional results. This paper proposes a customized convolutional neural network (CNN) for the detection of COVID-19 from chest X-rays called basicConv. This network consists of five sets of convolution and pooling layers, a flatten layer, and two dense layers with a total of approximately 9 million parameters. This network achieves an accuracy of 95.8%, which is comparable to other high-performing image classification networks. This provides a promising launching point for future research and developing a network that achieves an accuracy higher than that of the leading classification networks. It also demonstrates the incredible power of convolution. This paper is an extension of a 2022 Honors Thesis (Henderson, Joshua Elliot, "Convolutional Neural Network for COVID-19 Detection in Chest X-Rays” (2022). Honors Thesis. 254. https://red.library.usd.edu/honors-thesis/254 ). © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Attribute Analysis and Diagnosis of LUNG CT Images of COVID-19

Computed Tomography (CT) is an authoritative verification standard for patients with Corona Virus Disease 2019 (COVID-19). Automatic detection of lung infection through CT is of great significance for epidemic prevention and control and prevention of cross-infection. The accuracy of existing lung CT image segmentation methods is not high, and due to the privacy protection measures of hospitals, the number of COVID-19 lung CT data sets is too small, which is prone to over-fitting during training. In this paper, we propose a qualitative mapping model for the diagnosis and localization of COVID-19 lesions. The binary image processed by U-net network is used as input, and lung CT is segmented as four attributes, and attribute diagnosis is carried out with the help of correlation matrix and transformation degree function. Experiments show that this method not only avoids the over-fitting risk of data sets, but also increases the robustness of data. Experiments also prove that this design has higher accuracy than the simple neural network learning. © 2022 IEEE.

Automatic vascular detection system for the part of the forearm of patients

Currently the health system continues to fight against the pandemic caused by SARS-CoV2 because this virus has not yet disappeared and there are still outbreaks of infections in several places, this generated in people an indecision of what it can generate later since this virus generated a change in the world. Being a contagious and fast-spreading virus, WHO called on all governments to take appropriate measures to stop the spread of COVID-19 as there were many infected. Given this, there are people who need the care of a doctor because they suffer from a disease and that this implies the extraction of blood for a deep analysis or to place an intravenous injection in the patient's forearm, but in many cases the distribution of the veins can not be visualized and this hinders the work of the doctor. In view of this problem, in this article an automatic vascular detection system was carried out for the part of the forearm of patients and to be able to visualize the subcutaneous vein so that the doctor has access quickly and help the patient in an emergency. Through the development of the system, it was observed that it works in the best way, since in its development a 97.69% efficiency was obtained by showing the binary image where the distribution of the veins is observed taking 8.74 seconds, being an accepted value so that it can be implemented in several medical centers. © 2022 IEEE.

Lung Parenchyma Segmentation Based on CT Images

Novel Coronavirus targets the lung posing a serious threat to human health and causing huge social and economic losses. Extraction of lung parenchyma from CT images is an important step in the diagnosis of Novel Coronavirus. Therefore, accurate segmentation of lung parenchyma is highly significant for the diagnosis of disease. A lung parenchyma segmentation method based on OTSU and morphological operation is proposed. First of all, according to the CT image noise type, bilateral filtering is selected as preprocessing to filter out image noise. Then, binary images are obtained by the OTSU-based algorithm. Secondly, the residual interference of the trachea and blood vessels in the image is removed by morphological operation, and connected areas are marked and holes are filled. Finally, the original image is multiplied by the mask to obtain the lung parenchyma image. Experimental results show that this method can accurately segment lung parenchyma. © 2021 IEEE.

Diagnosing Covid-19 using AI based Medical Image Analysis

The pandemic of COVID-19 is currently one of the most significant problems being dealt with, all around the world. It mainly affects the lungs of the infected person which can further result in serious threats. So to avoid this life threatening condition, we have used chest radiological images for COVID-19 detection. This infectious disease is communicable and is spreading rapidly throughout the world. Hence, fast and accurate detection of COVID-19 is mandatory, so one can be given proper treatment well before time. In this paper, the proposed work aims to develop a web application, namely CovSADs(Covid-19 Smart A.I. Diagnosis System), using deep learning approach for faster and efficient detection of COVID-19. This web application uses X-ray and CT scan images for the evaluation. Here, we have developed DeepCovX and DeepCovCT models by incorporating Transfer Learning (TL) approach for COVID-19 detection via chest X-ray and CT scan images respectively. Further, we have used GradCam in case of X-ray to make sure our model is looking at relevant information to make decisions and image-segmentation is used in case of CT scan to extract and localize Region-of-interest (ROI) from binary image. Our proposed models show the accuracy of 95.89% and 98.01% for X-ray and CT scan images respectively. We have obtained specificity of 99.57%, sensitivity of 100%, and AUC of 0.998 in case of X-ray and specificity of 98.80%, sensitivity of 97.06%, and AUC of 0.9875 in case of CT scan images. F1-score is obtained as 0.98 for COVID-19 and 0.98 for Non-COVID-19 in case of CT scan images. Both quantitative and qualitative results demonstrate promising results for COVID-19 detection and extraction of infected lung regions. The primary objective of the web application is to assist the radiologists not only for mass screening but also to help in planning treatment process. © 2022 ACM.