COVID-19 Detection Using Multimodal and Multi-model Ensemble Based Deep Learning Technique

COVID-19 is a fatal disease that threatens the people's health worldwide in the last few years. Although the testing techniques for COVID-19 had become more widespread, they still lack the speed and accuracy of disease pattern detection. Thanks to Artificial Intelligence (AI) as it can accelerate the detection process by deep learning techniques that can be used to achieve high performance in COVID-19 identification. Many types of Convolutional Neural Networks (CNN) as the most image classification deep learning techniques are used for automatically diagnosing this disease using X-ray or Computerized Tomography (CT-scan) medical images. The individual CNN types can obtain good results with a specific type of images like X-ray or CT-scan images in a certain dataset but, it could not give the same quality for other types of images or datasets. Through this paper, multiple standards model and custom CNN model have been merged using ensemble method to enhance the overall performance, while the accuracy of each model is a parameter in majority voting. Consequently, the proposed method will started with an initial simple classifier to classify between X-ray image and CT-image then followed by the ensemble model, and lasted by the decision making algorithm. Using different image types like X-ray and CT-scan images from different dataset sources enhance the overall performance as will be cleared in our results. The proposed model has three main parts: Multimodal imaging data, Multi-model based CNN structure, and decision-making diffusion based on the Multi-model output part. The main objective of using multiple models or multiple algorithms in detecting COVID-19 is to decrease the error percentage and increase the validation accuracy. Testing and validation results assure that the performance of the proposed method for COVID-19 chest X-rays and CT-scan images outperforms the individual and classical CNN learners' design. © 2022 IEEE.

A decision support healthcare system based on IoT and neural network technique

Purpose: The purpose of this paper is to develop e-health and patient monitoring systems remotely to overcome the difficulty of patients going to hospitals especially in times of epidemics such as virus disease (COVID-19). Artificial intelligence (AI) technology will be combined Internet of Things (IoT) in this research to overcome these challenges. The research aims to select the most appropriate, best-hidden layers numbers and the activation function types for the neural network (NN). Then, define the patient data sent through protocols of the IoT. NN checks the patient’s medical sensors data to make the appropriate decision. Then it sends this diagnosis to the doctor. Using the proposed solution, the patients can diagnose and expect the disease automatically and help physicians to discover and analyze the disease remotely without the need for patients to go to the hospital. Design/methodology/approach: AI technology will be combined with the IoT in this research. The research aims to select the most appropriate’ best-hidden layers numbers’ and the activation function types for the NN. Findings: Decision support health-care system based on IoT and deep learning techniques was proposed. The authors checked out the ability to integrate the deep learning technique in the automatic diagnosis and IoT abilities for speeding message communication over the internet has been investigated in the proposed system. The authors have chosen the appropriate structure of the NN (best-hidden layers numbers and the activation function types) to build the e-health system is performed in this work. Also, depended on the data from expert physicians to learn the NN in the e-health system. In the verification mode, the overall evaluation of the proposed diagnosis health-care system gives reliability under different patient’s conditions. From evaluation and simulation results, it is clear that the double hidden layer of feed-forward NN and its neurons contain Tanh function preferable than other NN. Originality/value: AI technology will be combined IoT in this research to overcome challenges. The research aims to select the most appropriate, best-hidden layers numbers and the activation function types for the NN. © 2021, Emerald Publishing Limited.