IoT-enabled stacked ensemble of deep neural networks for the diagnosis of COVID-19 using chest CT scans

Abstract

The ongoing COVID-19 (novel coronavirus disease 2019) pandemic has triggered a global emergency, resulting in significant casualties and a negative effect on socioeconomic and healthcare systems around the world. Hence, automatic and fast screening of COVID-19 infections has become an urgent need of this pandemic. Real-time reverse transcription polymerase chain reaction (RT-PCR), a commonly used primary clinical method, is expensive and time-consuming for skilled health professionals. With the aid of various AI functionalities and advanced technologies, chest CT scans may thus be a viable alternative for quick and automatic screening of COVID-19. At the moment, significant advances in 5G cellular and internet of things (IoT) technology are finding use in various applications in the healthcare sector. This study presents an IoT-enabled deep learning-based stacking model to analyze chest CT scans for effective diagnosis of COVID-19 encounters. At first, patient data will be obtained using IoT devices and sent to a cloud server during the data procurement stage. Then we use different fine-tuned CNN sub-models, which are stacked together using a meta-learner to detect COVID-19 infection from input CT scans. The proposed model is evaluated using an open access dataset containing both COVID-19 infected and non-COVID CT images. Evaluation results show the efficacy of the proposed stacked model containing fine-tuned CNNs and a meta-learner in detecting coronavirus infections using CT scans.

5G in Healthcare: From COVID-19 to Future Challenges

“…The first report on CT scanning during COVID-19 pandemic was performed on 152 patients from Chengdu (Sichuan, China) and other centres in Sichuan province [7]. Subsequently multilayer platforms, based on edge devices or IoT and cloud computing, were proposed [37], [38].…”

“…CNNs were trained in the cloud while explainable AI was used in the edge layer to visualize COVID-19 in CT scans [37]. In another report, IoT devices were used to acquire patient data that were sent directly to a cloud server using 5G networks [38]. Several CNNs models were ensembled to boost performance of COVID-9 diagnosis from CT scans.…”

“…Several CNNs models were ensembled to boost performance of COVID-9 diagnosis from CT scans. The ensemble model was trained on 2484 sample CT scan and achieved a 96.6% diagnostic accuracy [38].…”

Automatic Mushroom Species Classification Model for Foodborne Disease Prevention Based on Vision Transformer

“…is study also explores the distribution of features generated by the transfer learning model to better understand their class separability [47,48]. e output of the high-dimensional layers was viewed using dimensionality reduction methods [49]. e t-SNE was presented by Van der Maaten and Hinton [50] in 2008 as a novel method for scaling down high-dimensional data.…”

“…Let X be a vector holding all samples in the dataset and let Y be a target vector representing the lowdimensional representation, as shown in Eq. 5 [49]. e similarity of data point x j to data point x i is described using the conditional probability P j|i in the original high-dimensional space, written as a conditional probability [50,51]:…”