Depth-wise dense neural network for automatic COVID19 infection detection and diagnosis

Abstract

Coronavirus (COVID-19) and its new strain resulted in massive damage to society and brought panic worldwide. Automated medical image analysis such as X-rays, CT, and MRI offers excellent early diagnosis potential to augment the traditional healthcare strategy to fight against COVID-19. However, the identification of COVID infected lungs X-rays is challenging due to the high variation in infection characteristics and low-intensity contrast between normal tissues and infections. To identify the infected area, in this work, we present a novel depth-wise dense network that uniformly scales all dimensions and performs multilevel feature embedding, resulting in increased feature representations. The inclusion of depth wise component and squeeze-and-excitation results in better performance by capturing a more receptive field than the traditional convolutional layer; however, the parameters are almost the same. To improve the performance and training set, we have combined three large scale datasets. The extensive experiments on the benchmark X-rays datasets demonstrate the effectiveness of the proposed framework by achieving 96.17% in comparison to cutting-edge methods primarily based on transfer learning.

Evaluating the impacts of COVID-19 outbreak on supply chain risks by modified failure mode and effects analysis: a case study in an automotive company

“…Since the beginning of the COVID-19 outbreak, the SARS-CoV-2 coronavirus that causes COVID-19 has mutated, resulting in different variants of the virus. The current COVID-19 and its new variants resulted in massive damage to all fields and organizations' businesses and brought panic worldwide (Qayyum, 2021 ; Queiroz & Fosso Wamba, 2021 ; Sharma, 2021b ). One of the unique characteristics of the COVID-19 outbreak is that it is the first long-term supply chain disruption in decades (Ivanov, 2021 ).…”

A novel explainable COVID-19 diagnosis method by integration of feature selection with random forest

“…In [ 61 ], a depth-wise deep learning method was proposed to reorganize of COVID-19 affected lungs regions.…”

Assessment of Machine Learning Techniques in IoT-Based Architecture for the Monitoring and Prediction of COVID-19

“…Figure 1 shows the layered architecture of IoT-fog-cloud computing. This paper suggests a tracking and monitoring system for COVID-19 that would gather information from IoT sensors in a time-sensitive manner [14,15]. The current research presents the incorporation of eight data prediction models-Neural Network, Support Vector Machine (SVM), Deep Neural Network, K-Nearest Neighbor (K-NN), OneR, Naive Bayes, Decision Table, and Long Short-Term Memory (LSTM)-to rapidly classify possible coronavirus instances from real-time data [16].…”