An H2O’s Deep Learning-Inspired Model Based on Big Data Analytics for Coronavirus Disease (COVID-19) Diagnosis

Abstract

The outbreak of coronavirus diseases has rabidly spread all over the world. The World Health Organization (WHO) has announced that coronavirus COVID-19 is an international pandemic. Big Data analytics tools must handle and analyse the massive amount of big medical data, generated daily, quickly due to the fact that time is very significant issue in healthcare applications. In addition, several deep learning algorithms are used along with big data analysis processes to help in detecting COVID-19 outbreaks and predicting their worldwide spread. Many researchers developed their models to diagnosis COVID-19 using Computed Tomography (CT) or X-ray imaging. This chapter presents a detailed discussion of Deep Learning and Big Data Analytics effects in containment of the disease. In addition, an H2O's Deep-Learning-inspired model based on Big Data analytics (DLBD-COV) is proposed for early diagnosis of COVID-19 cases using CT or Xray images. The proposed diagnosis model is build based on the machine learning framework (H2O) for scalable processing. The Generative Adversarial Networks (GAN) and the Convolutional Neural Networks (CNNs) are used and their classification results are compared. The experimental results emphasize the superiority of DLBD-COV when using H 2 O framework for scalable COVID19 classification. The results obtained, using a dataset with thousands of real data and images, show encouraging performance using the automated feature extraction of deep learning techniques used in DLBD-COV. Keywords Coronavirus disease (COVID-19) • CT imaging • X-Ray • Big data analytics • H 2 O • Deep learning technique • Generative adversarial networks (GAN) • Convolutional neural networks (CNNs)

A New Generation of ResNet Model Based on Artificial Intelligence and Few Data Driven and Its Construction in Image Recognition Model

“…Since Alexnet [ 11 ], the CNN structure has been deepening, and VGG and GoogLeNet [ 12 ] have 19 and 22 convolutional layers, respectively. With the increase of network depth, the existence of gradient disappearance problem makes the network training more difficult and the convergence result is not good, and then the ResNet network [ 13 ] is introduced, as shown in Figure 2 .…”

“…Therefore, the development of open, standardized, and intelligent [ 3 ] green tea classifications and identification methods is an inevitable trend. New classification and assessment methods for green tea have been emerging, such as physicochemical review methods [ 4 , 5 ], fingerprinting assessment methods [ 6 , 7 ], intelligent sensory review methods [ 8 , 9 ], and infrared spectral imaging technology detection methods [ 10 , 11 ], but these methods have their limitations to a certain extent, such as relevant instruments and cumbersome and complicated operations, and most of them are based on the overall tea leaves. It is necessary to propose an objective, simple, fast, and low-cost method for green tea classification, since most of them are based on the whole tea leaves for review, which requires specific and time-consuming requirements.…”

Combating COVID-19 Using Generative Adversarial Networks and Artificial Intelligence for Medical Images: Scoping Review

“…As shown in Table 2 , the included studies have reported 5 different tasks being addressed: augmentation (data augmentation), diagnosis of COVID-19, prognosis, segmentation (to identify the lung region), and diagnosis of lung diseases. As the diagnosis of COVID-19 using medical imaging has been a priority since the pandemic started, 39 (68%) of 57 studies reported the diagnosis of COVID-19 as the main focus of their work [ 19 - 21 , 23 - 33 , 35 - 37 , 39 , 41 , 42 , 44 , 46 , 50 , 52 , 53 , 55 , 56 , 58 - 60 , 63 - 69 , 71 , 72 ]. In addition, 9 (16%) studies reported data augmentation as the main task addressed in the work [ 18 , 43 , 45 , 49 , 54 , 61 , 62 ], 1 (2%) study reported prognosis of COVID-19 [ 22 ], 3 (5%) studies reported segmentation of lungs [ 34 , 51 , 57 ], and 1 (2%) study reported diagnosis of multiple lung diseases [ 47 ].…”

“…The augmented data were then used to improve the training of different CNNs to diagnose COVID-19. In addition, 3 (5%) studies used GANs for segmentation of the lung region within the chest radiology images [ 37 , 51 , 57 ], 3 (5%) studies used GANs for superresolution to improve the quality of the images before using them for diagnosis purposes [ 30 , 44 , 68 ], 5 (9%) studies used GANs for the diagnosis of COVID-19 [ 20 , 58 , 69 , 70 , 72 ], 2 (4%) studies used GANs for feature extraction from images [ 19 , 47 ], and 1 (2%) study used a GAN-based method for prognosis of COVID-19 [ 22 ]. The prevalent mode of imaging is the use of 2D imaging data, and 1 (2%) study reported a GAN-based method for synthesizing 3D data [ 49 ].…”

“…Specifically, the use of X-ray images dominated the studies. In total, 29 (51%) studies used X-ray images of lungs [ 20 , 21 , 25 , 27 - 29 , 31 , 32 , 35 , 37 , 40 - 43 , 45 , 50 , 52 , 54 , 56 , 57 , 59 , 60 , 62 , 64 , 65 , 67 , 70 , 73 , 74 ], while 21 (37%) studies used CT images [ 18 , 19 , 22 - 24 , 26 , 30 , 33 , 34 , 36 , 38 , 48 , 49 , 51 , 53 , 55 , 58 , 61 , 63 , 66 , 71 ], and 6 (11%) studies reported the use of both X-ray and CT images [ 39 , 44 , 46 , 47 , 68 , 72 ]. Only 1 (2%) study used ultrasound images for COVID-19 diagnosis [ 69 ], which shows that ultrasound is not a popular imaging modality for training GANs and other deep learning models for COVID-19 detection (also see Figure 4 ).…”