A review of deep learning-based detection methods for COVID-19

COVID-19 is a fast-spreading pandemic, and early detection is crucial for stopping the spread of infection. Lung images are used in the detection of coronavirus infection. Chest X-ray (CXR) and computed tomography (CT) images are available for the detection of COVID-19. Deep learning methods have been proven efficient and better performing in many computer vision and medical imaging applications. In the rise of the COVID pandemic, researchers are using deep learning methods to detect coronavirus infection in lung images. In this paper, the currently available deep learning methods that are used to detect coronavirus infection in lung images are surveyed. The available methodologies, public datasets, datasets that are used by each method and evaluation metrics are summarized in this paper to help future researchers. The evaluation metrics that are used by the methods are comprehensively compared.Scopu

Multi-descriptor random sampling for patch-based face recognition

While there has been a massive increase in research into face recognition, it remains a challenging problem due to conditions present in real life. This paper focuses on the inherently present issue of partial occlusion distortions in real face recognition applications. We propose an approach to tackle this problem. First, face images are divided into multiple patches before local descriptors of Local Binary Patterns and Histograms of Oriented Gradients are applied on each patch. Next, the resulting histograms are concatenated, and their dimensionality is then reduced using Kernel Principle Component Analysis. Once completed, patches are randomly selected using the concept of random sampling to finally construct several sub-Support Vector Machine classifiers. The results obtained from these sub-classifiers are combined to generate the final recognition outcome. Experimental results based on the AR face database and the Extended Yale B database show the effectiveness of our proposed technique

End-to-end image steganography using deep convolutional autoencoders

Image steganography is used to hide a secret image inside a cover image in plain sight. Traditionally, the secret data is converted into binary bits and the cover image is manipulated statistically to embed the secret binary bits. Overloading the cover image may lead to distortions and the secret information may become visible. Hence the hiding capacity of the traditional methods are limited. In this paper, a light-weight yet simple deep convolutional autoencoder architecture is proposed to embed a secret image inside a cover image as well as to extract the embedded secret image from the stego image. The proposed method is evaluated using three datasets - COCO, CelebA and ImageNet. Peak Signal-to-Noise Ratio, hiding capacity and imperceptibility results on the test set are used to measure the performance. The proposed method has been evaluated using various images including Lena, airplane, baboon and peppers and compared against other traditional image steganography methods. The experimental results have demonstrated that the proposed method has higher hiding capacity, security and robustness, and imperceptibility performances than other deep learning image steganography methods

Artificial Intelligence in Predicting Heart Failure

Heart Failure is a major chronic disease that is increasing day by day and a great health burden in health care systems world wide. Artificial intelligence (AI) techniques such as machine learning (ML), deep learning (DL), and cognitive computer can play a critical role in the early detection and diagnosis of Heart Failure Detection, as well as outcome prediction and prognosis evaluation. The availability of large datasets from difference sources can be leveraged to build machine learning models that can empower clinicians by providing early warnings and insightful information on the underlying conditions of the patient

A Deep Learning Framework for the Detection of Abnormality in Cerebral Blood Flow Velocity Using Transcranial Doppler Ultrasound

Transcranial doppler (TCD) ultrasound is a non-invasive imaging technique that can be used for continuous monitoring of blood flow in the brain through the major cerebral arteries by calculating the cerebral blood flow velocity (CBFV). Since the brain requires a consistent supply of blood to function properly and meet its metabolic demand, a change in CBVF can be an indication of neurological diseases. Depending on the severity of the disease, the symptoms may appear immediately or may appear weeks later. For the early detection of neurological diseases, a classification model is proposed in this study, with the ability to distinguish healthy subjects from critically ill subjects. The TCD ultrasound database used in this study contains signals from the middle cerebral artery (MCA) of 6 healthy subjects and 12 subjects with known neurocritical diseases. The classification model works based on the maximal blood flow velocity waveforms extracted from the TCD ultrasound. Since the signal quality of the recorded TCD ultrasound is highly dependent on the operator's skillset, a noisy and corrupted signal can exist and can add biases to the classifier. Therefore, a deep learning classifier, trained on a curated and clean biomedical signal can reliably detect neurological diseases. For signal classification, this study proposes a Self-organized Operational Neural Network (Self-ONN)-based deep learning model Self-ResAttentioNet18, which achieves classification accuracy of 96.05% with precision, recall, f1 score, and specificity of 96.06%, 96.05%, 96.06%, and 96.09%, respectively. With an area under the ROC curve of 0.99, the model proves its feasibility to confidently classify middle cerebral artery (MCA) waveforms in near real-time.This work was made possible by the High Impact grant of Qatar University # QUHI-CENG-22_23-548 and student grant: QUST-1-CENG-2023-796. The statements made herein are solely the responsibility of the authors.Scopu

A Novel Efficient Classwise Sparse and Collaborative Representation for Holistic Palmprint Recognition

Palmprint recognition is an important and widely used modality in biometric systems. It has a high reliability, stability and user acceptability. Although the discriminative ability of the existing state-of-the-art holistic techniques, their effectiveness heavily relies upon the quality of training data. Indeed, palmprint images contain different information including identity, illumination and distortions related to the acquisition systems. To overcome this problem, we explore a novel efficient holistic Classwise Sparse and Collaborative Representation (CSR). Extensive experiments have been performed on two existing and widely used palmprint datasets: multispectral and Poly U. The obtained experimental results demonstrated very encouraging performances when compared to state-of-the-art techniques. � 2018 IEEE.This publication was made possible using a grant from the Qatar National Research Fund through National Priority Research Program (NPRP) No. 8-140-2-065. The contents of this publication are solely the responsibility of the authors and do not necessarily represent the official views of the Qatar National Research Fund or Qatar University.Scopu

A comprehensive overview of feature representation for biometric recognition

The performance of any biometric recognition system heavily dependents on finding a good and suitable feature representation space where observations from different classes are well separated. Unfortunately, finding this proper representation is a challenging problem which has taken a huge interest in machine learning and computer vision communities. In the this paper we present a comprehensive overview of the different existing feature representation techniques. This is carried out by introducing simple and clear taxonomies as well as effective explanation of the prominent techniques. This is intended to guide the neophyte and provide researchers with state-of-the-art approaches in order to help advance the research topic in biometrics.This publication was made possible using a grant from the Qatar National Research Fund through National Priority Research Program (NPRP) # NPRP 8-140-2-065. The contents of this publication are solely the responsibility of the authors and do not necessarily represent the official views of the Qatar National Research Fund or Qatar University.Scopu