Starting from Wuhan in China at the end of 2019, coronavirus disease (COVID-19) has propagated fast all over the world, affecting the lives of billions of people and increasing the mortality rate worldwide in few months. The golden treatment against the invasive spread of COVID-19 is done by identifying and isolating the infected patients, and as a result, fast diagnosis of COVID-19 is a critical issue. The common laboratory test for confirming the infection of COVID-19 is Reverse Transcription Polymerase Chain Reaction (RT-PCR). However, these tests suffer from some problems in time, accuracy, and availability. Chest images have proven to be a powerful tool in the early detection of COVID-19. In the current study, a hybrid learning and optimization approach named CovH2SD is proposed for the COVID-19 detection from the Chest Computed Tomography (CT) images. CovH2SD uses deep learning and pre-trained models to extract the features from the CT images and learn from them. It uses Harris Hawks Optimization (HHO) algorithm to optimize the hyperparameters. Transfer learning is applied using nine pre-trained convolutional neural networks (i.e. ResNet50, ResNet101, VGG16, VGG19, Xception, MobileNetV1, MobileNetV2, DenseNet121, and DenseNet169). Fast Classification Stage (FCS) and Compact Stacking Stage (CSS) are suggested to stack the best models into a single one. Nine experiments are applied and results are reported based on the Loss, Accuracy, Precision, Recall, F1-Score, and Area Under Curve (AUC) performance metrics. The comparison between combinations is applied using the Weighted Sum Method (WSM). Six experiments report a WSM value above 96.5%. The top WSM and accuracy reported values are 99.31% and 99.33% respectively which are higher than the eleven compared state-of-the-art studies.

CovH2SD: A COVID-19 detection approach based on Harris Hawks Optimization and stacked deep learning.

https://doi.org/10.1016/j.compbiomed.2022.105383

An automated diagnosis and classification of COVID-19 from chest CT images using a transfer learning-based convolutional neural network

Hybrid deep learning and genetic algorithms approach (HMB-DLGAHA) for the early ultrasound diagnoses of breast cancer

In this paper we propose two novel deep convolutional network architectures, CovidResNet and CovidDenseNet, to diagnose COVID-19 based on CT images. The models enable transfer learning between different architectures, which might significantly boost the diagnostic performance. Whereas novel architectures usually suffer from the lack of pretrained weights, our proposed models can be partly initialized with larger baseline models like ResNet50 and DenseNet121, which is attractive because of the abundance of public repositories. The architectures are utilized in a first experimental study on the SARS-CoV-2 CT-scan dataset, which contains 4173 CT images for 210 subjects structured in a subject-wise manner into three different classes. The models differentiate between COVID-19, non-COVID-19 viral pneumonia, and healthy samples. We also investigate their performance under three binary classification scenarios where we distinguish COVID-19 from healthy, COVID-19 from non-COVID-19 viral pneumonia, and non-COVID-19 from healthy, respectively. Our proposed models achieve up to 93.87% accuracy, 99.13% precision, 92.49% sensitivity, 97.73% specificity, 95.70% F1-score, and 96.80% AUC score for binary classification, and up to 83.89% accuracy, 80.36% precision, 82.04% sensitivity, 92.07% specificity, 81.05% F1-score, and 94.20% AUC score for the three-class classification tasks. We also validated our models on the COVID19-CT dataset to differentiate COVID-19 and other non-COVID-19 viral infections, and our CovidDenseNet model achieved the best performance with 81.77% accuracy, 79.05% precision, 84.69% sensitivity, 79.05% specificity, 81.77% F1-score, and 87.50% AUC score. The experimental results reveal the effectiveness of the proposed networks in automated COVID-19 detection where they outperform standard models on the considered datasets while being more efficient.

COVID-Nets: deep CNN architectures for detecting COVID-19 using chest CT scans

Alzheimer’s disease (AD) is one of the most common neurodegenerative diseases. Accurate diagnosis of mild cognitive impairment (MCI) in the prodromal stage of AD can delay onset. Therefore, the early diagnosis of AD is particularly essential. The convolutional neural network (CNN) extracts feature of image layer-by-layer, and the observed features are obtained by setting different receptive fields. However, the brain structure is very complicated, and the etiology of AD is unknown, in addition, most of the existing methods do not consider the details and overall structure of the image. To address this issue, we propose a novel multi-scale convolutional neural network (MSCNet) to enhance the model’s feature representation ability. A channel attention mechanism is introduced to improve the interdependence between channels and adaptively recalibrate the channel direction’s characteristic response. To verify the effectiveness of our method, we segment the original MRI data to obtain white matter (WM) and gray matter (GM) datasets and train the model. Extensive experiments show that our method obtains the state-of-the-art performance with fewer parameters and lower computational complexity. 

Diagnosis of Alzheimer’s disease via an attention-based multi-scale convolutional neural network

In the current decade, advances in health care are attracting widespread interest due to their contributions to people longer surviving and fitter lives. Alzheimer’s disease (AD) is the commonest neurodegenerative and dementing disease. The monetary value of caring for Alzheimer’s disease patients is involved to rise dramatically. The necessity of having a computer-aided system for early and accurate AD classification becomes crucial. Deep-learning algorithms have notable advantages rather than machine learning methods. Many recent research studies that have used brain MRI scans and convolutional neural networks (CNN) achieved promising results for the diagnosis of Alzheimer’s disease. Accordingly, this study proposes a CNN based end-to-end framework for AD-classification. The proposed framework achieved 99.6%, 99.8%, and 97.8% classification accuracies on Alzheimer’s disease Neuroimaging Initiative (ADNI) dataset for the binary classification of AD and Cognitively Normal (CN). In multi-classification experiments, the proposed framework achieved 97.5% classification accuracy on the ADNI dataset.

A CNN based framework for classification of Alzheimer’s disease

Human action recognition techniques have gained significant attention among next-generation technologies due to their specific features and high capability to inspect video sequences to understand human actions. As a result, many fields have benefited from human action recognition techniques. Deep learning techniques played a primary role in many approaches to human action recognition. The new era of learning is spreading by transfer learning. Accordingly, this study’s main objective is to propose a framework with three main phases for human action recognition. The phases are pre-training, preprocessing, and recognition. This framework presents a set of novel techniques that are three-fold as follows, (i) in the pre-training phase, a standard convolutional neural network is trained on a generic dataset to adjust weights; (ii) to perform the recognition process, this pre-trained model is then applied to the target dataset; and (iii) the recognition phase exploits convolutional neural network and long short-term memory to apply five different architectures. Three architectures are stand-alone and single-stream, while the other two are combinations between the first three in two-stream style. Experimental results show that the first three architectures recorded accuracies of 83.24%, 90.72%, and 90.85%, respectively. The last two architectures achieved accuracies of 93.48% and 94.87%, respectively. Moreover, The recorded results outperform other state-of-the-art models in the same field.

/pdf/human-action-recognition-based-on-transfer-learning-approach-2acvz4wq9x.pdf

Human Action Recognition Based on Transfer Learning Approach

Accurate and fast detection of COVID-19 patients is crucial to control this pandemic. Due to the scarcity of COVID-19 testing kits, especially in developing countries, there is a crucial need to rely on alternative diagnosis methods. Deep learning architectures built on image modalities can speed up the COVID-19 pneumonia classification from other types of pneumonia. The transfer learning approach is better suited to automatically detect COVID-19 cases due to the limited availability of medical images. This paper introduces an Optimized Transfer Learning-based Approach for Automatic Detection of COVID-19 (OTLD-COVID-19) that applies an optimization algorithm to twelve CNN architectures to diagnose COVID-19 cases using chest x-ray images. The OTLD-COVID-19 approach adapts Manta-Ray Foraging Optimization (MRFO) algorithm to optimize the network hyperparameters' values of the CNN architectures to improve their classification performance. The proposed dataset is collected from eight different public datasets to classify 4-class cases (COVID-19, pneumonia bacterial, pneumonia viral, and normal). The experimental result showed that DenseNet121 optimized architecture achieves the best performance. The evaluation results based on Loss, Accuracy, F1-score, Precision, Recall, Specificity, AUC, Sensitivity, IoU, and Dice values reached 0.0523, 98.47%, 0.9849, 98.50%, 98.47%, 99.50%, 0.9983, 0.9847, 0.9860, and 0.9879 respectively.

An optimized transfer learning-based approach for automatic diagnosis of COVID-19 from chest x-ray images

Video transmission in peer-to-peer video-on-demand faces some challenges. These challenges include long transmission delay and poor quality of service. The peer selection plays an important role in enhancing transmission efficiency. For this reason, a proposed algorithm for peer selection is introduced to overcome these challenges. The proposed algorithm consists of four steps. First, the peers exchange their own buffer maps with other peers. Second, the requested segments are ordered according to their priorities. Third, neighbors of the receiver are evaluated by the efficiency estimation. Finally, the efficient sender list is applied to solve the overloading and bottleneck on the highest efficient sender. A simulation is introduced to evaluate the performance of the proposed algorithm compared to a peer selection algorithm with context-aware adaptive (CAA) data scheduling algorithm. The results show that, the proposed algorithm reduces initial buffering delay and achieves high throughput rather than CAA algorithm.

/pdf/a-proposed-peer-selection-algorithm-for-transmission-563dyumg5b.pdf

A Proposed Peer Selection Algorithm for Transmission Scheduling in P2P-VOD Systems

Segment transmission scheduling is essential in peer to peer video streaming. Transmission of the video segments faces some difficulties. Long playback buffering delay and high chunk miss ratio are the major ones of these difficulties. This paper introduces a proposed framework to overcome these difficulties. Moreover, it improves the overall performance of the streaming system. The framework is described in terms of four phases; in the first phase, unavailable segments and the partners for each segment are specified. By the end of the second phase, the priorities of unavailable segments are estimated according to segments' deadline time and number of available partners. In the third phase, the available partners are evaluated according to the bandwidth and the reputation estimation. Finally, the segment's sender is selected from the available partners according to the considerations that improve the performance of P2P video streaming. A simulation results are achieved to compare the performance of the proposed framework algorithms with respect to previous algorithms. The simulation results showed that proposed framework algorithms outperform previous algorithms.

Waleed M. Bahgat

Papers

A CNN based framework for classification of Alzheimer’s disease

Human Action Recognition Based on Transfer Learning Approach

An optimized transfer learning-based approach for automatic diagnosis of COVID-19 from chest x-ray images

A Proposed Peer Selection Algorithm for Transmission Scheduling in P2P-VOD Systems

A proposed framework for segment transmission scheduling in P2P video streaming