Book chapter (B2)

Advanced pattern recognition tools for disease diagnosis




List of Authors: Subasi Abdulhamit, Panigrahi Siba Smarak, Patil Bhalchandra Sunil, Canbaz Abdullah, Klén Riku

Publication year: 2022

Book title *: 5G IoT and Edge Computing for Smart Healthcare

ISBN: 978-0-323-90548-0

DOI: http://dx.doi.org/10.1016/B978-0-323-90548-0.00011-5

URL: https://doi.org/10.1016/B978-0-323-90548-0.00011-5


Abstract

Machine learning (ML) uses statistical theory to create models from data samples. Using the predictive and statistical models, computers can clean and curate the data, interpret and predict the outcomes of certainties (or uncertainties) with precise accuracy. Of course, the interpretation of the produced results and algorithmic solution designed for each problem needs to be fine-tuned and proficient for the target problem. Biomedical images relevant to different diseases are recorded from a body and are generally employed to diagnose precise physiological or pathological conditions. The objective of biomedical image analysis is exact modeling by using pattern recognition and computer vision to diagnose diseases by employing ML techniques. This chapter explains how artificial intelligence (AI) and ML techniques are utilized in disease diagnosis. An automated COVID-19 diagnosis approach based on deep feature extraction is also presented. After extracting features using deep transfer learning (DTL), the X-ray images are fed into the shallow ML model to diagnose COVID-19 from X-ray images. With chest X-ray, a patient can be identified as a potential COVID-19 patient and can be quarantined. X-ray equipment are already accessible in most hospitals, and already digitized. Since X-ray images are high dimensional data, a Convolutional Neural Network based feature extraction via transfer learning models are appropriate for the diagnosis of COVID-19. It may help an inpatient environment where the existing programs find it difficult to determine whether to keep the patient inward with other patients or separate them. This technique will also help classify patients with high COVID-19 risk who need to repeat testing with a false negative RT-PCR.


Last updated on 2022-23-09 at 14:25