Detection of various lung diseases including COVID-19 using extreme learning machine algorithm based on the features extracted from a lightweight CNN architecture.

Around the world, several lung diseases such as pneumonia, cardiomegaly, and tuberculosis (TB) contribute to severe illness, hospitalization or even death, particularly for elderly and medically vulnerable patients. In the last few decades, several new types of lung-related diseases have taken the lives of millions of people, and COVID-19 has taken almost 6.27 million lives. To fight against lung diseases, timely and correct diagnosis with appropriate treatment is crucial in the current COVID-19 pandemic. In this study, an intelligent recognition system for seven lung diseases has been proposed based on machine learning (ML) techniques to aid the medical experts. Chest X-ray (CXR) images of lung diseases were collected from several publicly available databases. A lightweight convolutional neural network (CNN) has been used to extract characteristic features from the raw pixel values of the CXR images. The best feature subset has been identified using the Pearson Correlation Coefficient (PCC). Finally, the extreme learning machine (ELM) has been used to perform the classification task to assist faster learning and reduced computational complexity. The proposed CNN-PCC-ELM model achieved an accuracy of 96.22% with an Area Under Curve (AUC) of 99.48% for eight class classification. The outcomes from the proposed model demonstrated better performance than the existing state-of-the-art (SOTA) models in the case of COVID-19, pneumonia, and tuberculosis detection in both binary and multiclass classifications. For eight class classification, the proposed model achieved precision, recall and fi-score and ROC are 100%, 99%, 100% and 99.99% respectively for COVID-19 detection demonstrating its robustness. Therefore, the proposed model has overshadowed the existing pioneering models to accurately differentiate COVID-19 from the other lung diseases that can assist the medical physicians in treating the patient effectively.

Polarization-maintaining low-loss porous-core spiral photonic crystal fiber for terahertz wave guidance.

A polarization-maintaining porous-core spiral photonic crystal fiber is proposed for efficient transmission of polarization-maintaining terahertz (THz) waves. The finite element method with perfectly matched layer boundary conditions is used to characterize the guiding properties. We demonstrate that by creating artificial asymmetry in the porous core, an ultrahigh birefringence of 0.0483 can be obtained at the operating frequency of 1.0 THz. Moreover, a low effective material loss of 0.085 cm-1 and very small confinement loss of 1.91×10-3 dB/cm are achieved for the y-polarization mode with optimal design parameters. This article also focuses on some crucial design parameters such as power fraction, bending loss, and dispersion for usability in the THz regime.