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DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

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A novel multi-scale based deep convolutional neural network for detecting COVID-19 from X-rays.

Mohan Karnati¹, Ayan Seal¹, Geet Sahu¹

¹Department of Computer Science and Engineering, PDPM Indian Institute of Information Technology Design & Manufacturing Jabalpur, Jabalpur, Madhya Pradesh 482005, India.

Applied Soft Computing

|June 13, 2022

Summary

A new deep neural network (DNN) model effectively diagnoses COVID-19 lung infections from chest X-rays (CXRs). This AI-powered system offers rapid and accurate remote assessment, crucial for pandemic response and treatment evaluation.

Keywords:

COVID-19 Chest X-ray Deep neural network Internet of things

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Area of Science:

Medical Imaging
Artificial Intelligence
Public Health

Background:

The COVID-19 pandemic severely impacted global health, with respiratory tract infection being a primary concern.
Chest X-ray (CXR) is a preferred diagnostic tool due to its availability, speed, and cost-effectiveness compared to other methods.
Existing diagnostic systems face limitations in effectively managing widespread pandemics and enabling remote patient monitoring.

Purpose of the Study:

To develop an end-to-end IoT infrastructure for remote COVID-19 diagnosis during pandemics.
To design and implement a novel deep neural network (DNN) for intelligent interpretation of CXR images to assess COVID-19 severity.
To improve measurement science and limit disease dissemination through remote diagnostic capabilities.

Main Methods:

An IoT infrastructure was designed and implemented, comprising six distinct steps.
A novel deep neural network (DNN) utilizing multi-scale sampling filters was developed to analyze CXR images.
The DNN was trained and validated on five diverse, publicly available COVID-19 CXR datasets.

Main Results:

The proposed DNN achieved high classification accuracies across five datasets, ranging from 96.01% to 100%.
The model demonstrated rapid testing times, with some as low as 0.202 seconds.
The DNN outperformed fourteen existing baseline techniques in CXR-based COVID-19 diagnosis.

Conclusions:

The developed DNN model shows significant potential for accurate and efficient COVID-19 diagnosis using CXR images.
The system's ability to provide rapid, remote assessment is valuable for evaluating treatment efficacy, especially in underserved or remote areas.
This AI-driven approach contributes to enhancing public health infrastructure for pandemic preparedness and response.