A Robust Automated Framework for Classification of CT Covid-19 Images Using MSI-ResNet

Nowadays, the COVID-19 virus disease is spreading rampantly. There are some testing tools and kits available for diagnosing the virus, but it is in a limited count. To diagnose the presence of disease from radiological images, automated COVID-19 diagnosis techniques are needed. The enhancement of AI (Artificial Intelligence) has been focused in previous research, which uses X-ray images for detecting COVID-19. The most common symptoms of COVID-19 are fever, dry cough and sore throat. These symptoms may lead to an increase in the rigorous type of pneumonia with a severe barrier. Since medical imaging is not suggested recently in Canada for critical COVID-19 diagnosis, computer-aided systems are implemented for the early identification of COVID-19, which aids in noticing the disease progression and thus decreases the death rate. Here, a deep learning-based automated method for the extraction of features and classification is enhanced for the detection of COVID-19 from the images of computer tomography (CT). The suggested method functions on the basis of three main processes: data preprocessing, the extraction of features and classification. This approach integrates the union of deep features with the help of Inception 14 and VGG-16 models. At last, a classifier of Multi-scale Improved ResNet (MSI-ResNet) is developed to detect and classify the CT images into unique labels of class. With the support of available open-source COVID-CT datasets that consists of 760 CT pictures, the investigational validation of the suggested method is estimated. The experimental results reveal that the proposed approach offers greater performance with high specificity, accuracy and sensitivity. © 2023 CRL Publishing. All rights reserved.

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

Pneumonia image classification based on convolutional neural network

This letter introduces an improved convolutional neural network (CNN), which is used to classify and recognize different types of pneumonia using chest CT images. This classifying model is built and trained on thousands of real clinical chest CT images, which respectively belong to patients with viral pneumonia, patients with bacterial pneumonia, patients with COVID-19, and nonpatients. To richen the dataset and avoid over-fitting, pre-processing methods are recommended. Then the paper elaborates the structure of the new network and compares the performance of different optimizers in this dataset. Finally, the accuracy, specificity, precision, sensitivity, and F1-score of the model are calculated to quantitatively evaluate the performance of this model. The final training accuracy is about 97.9%, and the test accuracy is 91.8%. © 2022 IEEE.

Researches Advanced in Medical Detection based on Deep Learning

Medical image analysis based on computer vision technology has always been a research hotspot in the community, which aims to assist doctors in diagnosis by accurately analyzing pathological images to divide the patient's condition and the patient's lesions. Thanks to the rapid development of deep learning, the application of computer image recognition technology in medicine is becoming more and more widespread, while still facing a series of challenges such as low data set data, insufficient performance of algorithms and fine delineation of lesions. In order to solve these problems, based on extensive literature research, this paper first compares the algorithms in the application for Corona Virus Disease 2019, skin cancer and liver cancer. Then we introduce the improvement of these algorithms by expanding the number of data sets, optimizing the algorithms, and fitting the neural networks and models, whcih can improve the accuracy of image recognition technology to assist doctors in identifying lesions in clinical practice. The algorithms are further compared quantitatively on the basis of the training set in clinical diseases, and the difficulties to be overcome in image recognition and the future development trend are explained and predicted from the analysis of the comparison. Many new algorithms and excellent models are being gradually improved with the development of the times, and image recognition technology will also develop towards more research fields in the future. © 2023 SPIE.

Mask Detection for Covid-19 under Extreme Conditions

Mask detection has become a hot topic since the COVID-19 pandemic began in recent years. However, most scholars only focus on the speed and accuracy of detection, and fail to pay attention to the fact that mask detection is not suitable for people living under extreme conditions due to the degraded image quality. In this work, a denoising convolutional auto-encoder, a multitask cascaded convolutional networks (MTCNN) and a MobileNet were used to solve the problem of mask detection for COVID-19 under extreme environments. First of all, a network based on AlexNet is designed for the auto-encoder. This study found that the two-layer max pooling layers in AlexNet could not accurately extract image features but damage the quality of restored image. Therefore, they were deleted, and other parameters such as channel number were also modified to fit the new net, and finally trained using cosine distance. In addition, for MTCNN, this study changed the output condition of ONet from thresholding to maximum return, and lowered the thresholds of PNet and RNet to solve the problem that faces might not be found in low-quality images with mask and other covers. Furthermore, MobileNet was trained using categorical cross entropy loss function with adam optimizer. In the end, the accuracy of system for the photos captured under extreme conditions enhance from 50 % to 85% in test images. © 2022 IEEE.

Convolutional neural networks for imbalanced chest X-ray images classification

The COVID-19 epidemic has spread throughout the world and poses a serious threat to human health. Any technical device that provides the accurate and rapid automated diagnosis of COVID-19 can be extremely beneficial to healthcare providers. A new workflow for performing automated diagnosis is proposed in this paper. The proposed methods are built on a well-designed framework, two kinds of CNN architectures including a custom CNN and a pre-trained CNN are utilized to verify the effectiveness of the focal loss function. According to the experimental findings, both CNNs that were enhanced with the focal loss function converged faster and achieved higher accuracy on the test set, outperformed the models that utilized cross-entropy loss that does not consider the class-imbalanced issue in the multi-class image classification with imbalanced Chest X-ray(CXR) image datasets. In addition, image enhancement techniques turned out to be very helpful for enhancing the CXR image signatures to achieve better performance in our work. © 2023 SPIE.

Improved Method of X-Ray Image Classification Using Deep Learning for Covid-19 Detection

COVID-2019, which popped up in December 2019 in Wuhan, China. It quickly spread around the world and turned into a pandemic.It has wreaked havoc on people's daily lives and public health. It has wreaked havoc on people's everyday lives, health, and the economic growth. Positive cases should be found as early as possible in order to control the disease outbreak and treat those who have been infected as fast as possible. Because there are no precise toolkits available, the demand for additional diagnostic tools has increased significantly. Current findings from radiology imaging techniques suggest that such images can reveal a lot about the COVID virus. The use of modern AI technologies (Artificial intelligence) algorithms in conjunction with imaging techniques can help to identify this disease accurately. This paper presents a new model for automatically detecting COVID-19 from X-ray pictures. The suggested model was created to deliver precise diagnostics for three classes of categorization. © 2022 IEEE.