Automated diagnosis of COVID stages using texture-based Gabor features in variational mode decomposition from CT images

COVID-19 is a deadly and fast-spreading disease that makes early death by affecting human organs, primarily the lungs. The detection of COVID in the early stages is crucial as it may help restrict the spread of the progress. The traditional and trending tools are manual, time-inefficient, and less accurate. Hence, an automated diagnosis of COVID is needed to detect COVID in the early stages. Recently, several methods for exploiting computed tomography (CT) scan pictures to detect COVID have been developed;however, none are effective in detecting COVID at the preliminary phase. We propose a method based on two-dimensional variational mode decomposition in this work. This proposed approach decomposes pre-processed CT scan pictures into sub-bands. The texture-based Gabor filter bank extracts the relevant features, and the student's t-value is used to recognize robust traits. After that, linear discriminative analysis (LDA) reduces the dimensionality of features and provides ranks for robust features. Only the first 14 LDA features are qualified for classification. Finally, the least square- support vector machine (SVM) (radial basis function) classifier distinguishes between COVID and non-COVID CT lung images. The results of the trial showed that our model outperformed cutting-edge methods for COVID classification. Using tenfold cross-validation, this model achieved an improved classification accuracy of 93.96%, a specificity of 95.59%, and an F1 score of 93%. To validate our proposed methodology, we conducted different relative experiments with deep learning and traditional machine learning-based models like random forest, K-nearest neighbor, SVM, convolutional neural network, and recurrent neural network. The proposed model is ready to help radiologists identify diseases daily. © 2023 Wiley Periodicals LLC.

Gabor Filter-Based Caries Image Feature Analysis Using Machine Learning

Dental caries sufferers in Indonesia demonstrate a higher frequency than other dental diseases even before the Covid-19 pandemic. The high risk of spreading the virus during the pandemic hinders handling dental care patients. Teledentistry is suggested as the main alternative to reduce the risk of spreading the virus. This study aims to establish a system for classifying the level of dental caries based on texture applicable for clinical implementation. Dental caries images were extracted using the Gabor Filter method and classified using the Support Vector Machine (SVM) and K-Nearest Neighbor (K-NN). A downsampling technique was applied to this system to reduce the large number of features affecting the classification time. System testing revealed that the Cubic SVM model generated the best result: Accuracy of 90.5%, precision of 89.75%, recall of 89.25%, specificity of 91.75%, and f-score of 88.5%. © 2022 IEEE.

An Advanced Deep Learning Medical Image Recognition and Diagnosis of Respiratory System Viruses

Respiratory infections are a confusing and time-consuming task that caused recently a pandemic that affected the whole world. One of the pandemics was COVID-19 that has exposed the vulnerability of medical services across the world, particularly in underdeveloped nations. There comes a strong demand for developing new computer-assisted diagnosis tools to present cost-effective and rapid screening in locations wherein enormous traditional testing is impossible. Medical imaging becomes critical for diagnosing disease, X-rays and computed tomography (CT) scan are employed in the deep network which will be helpful in diagnosing diseases. This paper proposes a scanning model based on using a Mel Frequency Cepstral Coefficients (MFCC) features extracted from a respiratory virus CT-Scan image and then filtered by applying Gabor filter (GF). The filtered image is passed to Convolutional Neural Network (CNN) for classifying the image for the presence of a respiratory virus such as Covid, Viral Pneumonia or being a healthy normal image. The proposed system achieved a validation accuracy of 100% with an overall accuracy of 99.44%. © 2022 IEEE.

Chaotic Flower Pollination with Deep Learning Based COVID-19 Classification Model

The Coronavirus Disease (COVID-19) pandemic has exposed the vulnerabilities of medical services across the globe, especially in underdeveloped nations. In the aftermath of the COVID-19 outbreak, a strong demand exists for developing novel computer-assisted diagnostic tools to execute rapid and cost-effective screenings in locations where many screenings cannot be executed using conventional methods. Medical imaging has become a crucial component in the disease diagnosis process, whereas X-rays and Computed Tomography (CT) scan imaging are employed in a deep network to diagnose the diseases. In general, four steps are followed in image-based diagnostics and disease classification processes by making use of the neural networks, such as network training, feature extraction, model performance testing and optimal feature selection. The current research article devises a Chaotic Flower Pollination Algorithm with a Deep Learning-Driven Fusion (CFPADLDF) approach for detecting and classifying COVID-19. The presented CFPA-DLDF model is developed by integrating two DL models to recognize COVID-19 in medical images. Initially, the proposed CFPA-DLDF technique employs the Gabor Filtering (GF) approach to pre-process the input images. In addition, a weighted voting-based ensemble model is employed for feature extraction, in which both VGG-19 and the MixNet models are included. Finally, the CFPA with Recurrent Neural Network (RNN) model is utilized for classification, showing the work's novelty. A comparative analysis was conducted to demonstrate the enhanced performance of the proposed CFPADLDF model, and the results established the supremacy of the proposed CFPA-DLDF model over recent approaches. © 2023 Tech Science Press. All rights reserved.

Research on Prediction Model of COVID-19 Based on Deep Learning

The accurate prediction of COVID-19 is of great significance for the prevention and control of the epidemic. Based on the current situation and demand of COVID-19 prediction research, this paper mainly analyzes the convolutional neural network (CNN) model by using the deep learning algorithm, It uses 1dcnn model and Gabor filter to build the G-ldcnn model, and introduces back propagation to update The model has the high-efficiency learning ability of CNN model and the feature extraction ability of Gabor filter at the model. The model has the high-efficiency learning ability of CNN model and the feature extraction ability of Gabor filter at the same time, improves the prediction efficiency of the model while ensuring the accuracy, and can better adapt to By comparing the prediction model proposed in this paper with the current By comparing the prediction model proposed in this paper with the current mature model, it shows that the improved and optimized model has a high accuracy. © 2022 IEEE.

Detection of COVID-19 Using Denoising Autoencoders and Gabor Filters

As of 2019, COVID-19 is the most difficult issue that we are facing. Till now, it has reached over 30 million deaths. Since SARS-CoV-2 is the new virus, it took time to investigate and examine the influence of Coronavirus in human. After analyzing the spreading and infection of COVID-19, researchers applied Artificial Intelligence (AI) techniques to detect COVID-19 quickly to balance the rapid spreading of the virus. Image segmentation is a critical first step in clinical implementations, is a vital role in computer - aided diagnosis that relies heavily on image recognition. Image segmentation is used in medical MRI research to determine the proportions of different anatomical areas of the tissue, as well as how they change as the disease progresses. CT scans are often used to aid with diagnoses. Computer-assisted therapy (CAD) using AI is a particularly significant research area in intelligent healthcare. This paper presents the detection of COVID-19 at an early stage using autoencoders algorithm and Generative Adversarial Networks (GAN) using deep learning approach with more accurate results. The images of Chest Radiograph (CRG) and Chest Computed Tomography (CCT) are used as a trained dataset to detect since SARS-CoV-2 first affect the respiratory system in humans. We achieved a ratio of 1.0, 0.99, and 0.96, the combined dataset was randomly divided into the train, validation, and test sets. Although the early detection of Coronavirus is still a question since the accuracy of the deep learning approach is still under research. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Local Pre-Conditioning and Quality Enhancement to Handle Different Data Complexities in Contactless Fingerprint Classification

Biometric authentication systems have always been a fascinating approach to meet personalized security. Among the major existing solutions fingerprint-biometrics have gained widespread attention;yet, guaranteeing scalability and reliability over real-time demands remains a challenge. Despite innovations, the recent COVID-19 pandemic has capped the efficacy of the existing touch-based two-dimensional fingerprint detection models. Though, touchless fingerprint detection is considered as a viable alternative;yet, the real-time data complexities like non-linear textural patterns, dusts, non-uniform local conditions like illumination, contrast, orientation make it complex for realization. Moreover, the likelihood of ridge discontinuity and spatio-temporal texture damages can limit its efficacy. Considering these complexities, here, we focused on improving the input image intrinsic feature characteristics. More specifically, applied normalization, ridge orientation estimation, ridge frequency estimation, ridge masking and Gabor filtering over the input touchless fingerprint images. The proposed model mainly focusses on reducing FPR & EER by dividing the input image in to blocks and classify each input block as recoverable and nonrecoverable image block. Finally, an image with higher recoverable blocks with sufficiently large intrinsic features were considered for feature extraction and classification. The Proposed method outperforms when compared with the existing state of the art methods by achieving an accuracy of 94.72%, precision of 98.84%, recall of 97.716%, F-Measure 0.9827, specificity of 95.38% and a reduced EER of about 0.084. © 2022, International Journal of Advanced Computer Science and Applications. All Rights Reserved.

Multi-lane LBP-Gabor Capsule Network with K-means Routing for Medical Image Analysis

Medical images naturally occur in smaller quantities and are not balanced. Some medical domains such as radiomics involve the analysis of images to diagnose a patient’s condition. Often, images of sick inaccessible parts of the body are taken for analysis by experts. However, medical experts are scarce, and the manual analysis of the images is time-consuming, costly, and prone to errors. Machine learning has been adopted to automate this task, but it is tedious, time-consuming, and requires experienced annotators to extract features. Deep learning alleviates this problem, but the threat of overfitting on smaller datasets and the existence of the “black box” still lingers. This paper proposes a capsule network that uses Local Binary Pattern (LBP), Gabor layers, and K-Means routing in an attempt to alleviate these drawbacks. Experimental results show that the model produces state-of-the-art accuracy for the three datasets (KVASIR, COVID-19, and ROCT), does not overfit on smaller and imbalanced datasets, and has reduced complexity due to fewer parameters. Layer activation maps, a cluster of features, predictions, and reconstruction of the input images, show that our model is interpretable and has the credibility and trust required to gain the confidence of practitioners for deployment in critical areas such as health.

A Traditional Machine Learning Approach for COVID-19 Detection from CT Images

COVID-19 has severe effects on several body organs, especially the lung. These severe effects result in features in the COVID-19 patients' Computed Tomography (CT) images distinct from other viral pneumonia. Although the primary diagnosis of COVID-19 is not primarily screened by CT, machine learning-based diagnosis systems early detect the COVID-19 lung abnormalities. Feature extraction is crucial for the success of traditional machine learning algorithms. Traditional machine learning algorithms utilize hand-crafted features to identify and classify patterns in an image. This paper utilizes the Gabor filters as the primary feature extractor for automated COVID-19 classification from lung CT images. We use a publicly available COVID-19 data-set of chest CT images to validate the performance and accuracy of the proposed model. The Gabor filter and other feature extractors with Random Forest classifiers achieved over 81% classification accuracy, the sensitivity of 81%, Specificity of 82%, and F1 score of 81%. © 2021 IEEE.

Smart and Contactless Person Identification System in the COVID 19 Pandemic Process

The Covid 19 Pandemic has had an impact on many aspects of our daily lives such as Restricting contact through touch, wearing masks, practicing social distancing, staying indoors which has led to change in our behaviors and prioritized the importance of safety hygiene. We travel to different places such as Schools, Colleges, Restaurants, offices, and Hospitals. How do we adapt to these changes and refrain from getting the virus? Luckily, we have the technology to aid us. We are all used to biometric systems for marking our Presence/ Attendance in places like colleges, Offices, and Schools with fingerprint sensors, fingerprint sensors use our Fingerprint to mark our presence however Covid 19 has restricted the use of touch causing problems in marking attendance. One way to resolve the problem is using Artificial Intelligence by using a Recognizer to identify people with their face and iris features. We implement the Face Recognition and the Iris Recognition using two models which run concurrently, one to Recognize the Face by extracting the features of the face and passing the 128-d points to the Neural Network (Mobile net and Resnet Architecture). which gives the identity of the person whose image was matched with the trained database and the other by extracting iris features to recognize people. For extracting iris features we use the Gabor filter to extract features from the eyes which are then matched in the database for recognition using 3 distance-based matching algorithms city block distance, Euclidean distance, and cosine distance which gives an accuracy of 88.19%, 84.95%, and 85.42% respectively. The face Recognizer model yields an Accuracy of 98%, while Iris Recognizer yields an accuracy of 88%. When these models run concurrently it yields an accuracy of 92.4%. © 2021 IEEE.

A Physician Assisting COVID-19 Diagnosis System to Categorize Positive Cases using Radiology Images

The objective of the proposed work deals with assisting the doctors by providing the required pre-diagnosis data of COVID-19 patients using radiology images of the targeted patients. A machine learning approach is utilized to evaluate the radiology images of COVID-19 patients which performs preprocessing, segmentation, feature extraction and classification. The proposed work also deals with predetermined evaluation results of COVID-19 patients which give the stages of the COVID-19 patients stating from STAGE 1 through STAGE 4. It will help the physicians for the easy diagnosis of the COVID-19 in patients. A conventional machine learning approach based classification is performed in first pass which discriminates the patients as normal patients or COVID-19 positive patients. Initially, the pre-processing of the input radiology images is carried out to enhance the quality of the images. Second order statistical textural features are extracted using Gabor filter bank Finally, the extracted features are used to classify the COVID-19 positive and negative patients using a simple decision tree classifier. During second pass, the affected portion of the lung is segmented, and amount of infection is estimated through the evaluation of length and width of the affected lung portions. Now, the COVID-19 positive cases will be given higher priority to undergo second level of diagnosis and treatment processes by the physicians whereas the COVID-19 negative cases will undergo for continuous observation. Thus, the proposed diagnosis system will help the physician to speed up their service towards COVID-19 positive patients. © 2021 IEEE.