CO-IRv2: Optimized InceptionResNetV2 for COVID-19 detection from chest CT images

Abstract

This paper focuses on the application of deep learning (DL) in the diagnosis of coronavirus disease (COVID-19). The novelty of this work is in the introduction of optimized InceptionResNetV2 for COVID-19 (CO-IRv2) method. A part of the CO-IRv2 scheme is derived from the concepts of InceptionNet and ResNet with hyperparameter tuning, while the remaining part is a new architecture consisting of a global average pooling layer, batch normalization, dense layers, and dropout layers. The proposed CO-IRv2 is applied to a new dataset of 2481 computed tomography (CT) images formed by collecting two independent datasets. Data resizing and normalization are performed, and the evaluation is run up to 25 epochs. Various performance metrics, including precision, recall, accuracy, F1-score, area under the receiver operating characteristics (AUC) curve are used as performance metrics. The effectiveness of three optimizers known as Adam, Nadam and RMSProp are evaluated in classifying suspected COVID-19 patients and normal people. Results show that for CO-IRv2 and for CT images, the obtained accuracies of Adam, Nadam and RMSProp optimizers are 94.97%, 96.18% and 96.18%, respectively. Furthermore, it is shown here that for the case of CT images, CO-IRv2 with Nadam optimizer has better performance than existing DL algorithms in the diagnosis of COVID-19 patients. Finally, CO-IRv2 is applied to an X-ray dataset of 1662 images resulting in a classification accuracy of 99.40%.

Spiking Neural Networks Trained via Proxy

“…In recent years, deep learning in ANNs has been a revolution in the field of machine learning [46], [47] however researchers are still struggling to develop efficient learning algorithms for SNNs with deep architectures [5]. In this paper we proposed a proxy learning method for deep convolutional spiking neural networks.…”

Federated learning: Applications, challenges and future directions

“…Classification algorithms in machine learning [70] learn how to categorize or annotate a given collection of occurrences with labels or classes. For example, the classification tasks of COVID-19 detections [71][72][73][74][75][76][77][78][79][80][81][82][83][84], cancer diagnoses [85][86][87][88][89][90][91][92][93][94] and autism spectrum disorder (ASD) [84,[95][96][97] are considered in a FL setting in healthcare. Other classification tasks studied include emotion recognition and human activity [98][99][100] and prediction of patient hospitalization [101,102].…”

E-GCS: Detection of COVID-19 through classification by attention bottleneck residual network

“…Lastly, it has been employed to 1662 X-ray images leading to 99.4% accuracy. Though effective results have been attained, it has to be assessed for huge COVID-19 datasets ( Mondal et al, 2021 ).…”