Abstract
The COVID-19 pandemic has attracted the attention of big data analysts and artificial intelligence engineers. The classification of computed tomography (CT) chest images into normal or infected requires intensive data collection and an innovative architecture of AI modules. In this article, we propose a platform that covers several levels of analysis and classification of normal and abnormal aspects of COVID-19 by examining CT chest scan images. Specifically, the platform first augments the dataset to be used in the training phase based on a reliable collection of images, segmenting/detecting the suspicious regions in the images, and analyzing these regions in order to output the right classification. Furthermore, we combine AI algorithms, after choosing the best fit module for our study. Finally, we show the effectiveness of this architecture when compared to other techniques in the literature. The obtained results show that the accuracy of the proposed architecture is 95%.
Highlights
Artificial intelligence has been a great contributor to the field of medical diagnosis and the design of new medications
The segmentation process is a foundation in the realm of COVID-19 apps as it makes radiologists’ lives much easier; it provides them with accurate recognition of regions of interest and reliable diagnosis of the virus
It depends on the volume of the lungs and the volume of the infected part evaluated by the segmentation block
Summary
Artificial intelligence has been a great contributor to the field of medical diagnosis and the design of new medications. The development of automated diagnostic systems enhances the accuracy and speed of the diagnosis, and protects workers in the health sector by notifying them of the condition severity of each infected patient (Alimadadi et al, 2020). In this context, we are proposing an AI medical platform that aims to collect multimodal data from different sources, integrating both network and medical sensory systems, with the goal to efficiently participate in the worldwide fight against this pandemic.
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