ABSTRACT
Due to the recent COVID-19 pandemic that occurred worldwide since 2020, scientists and researchers have been studying methods to detect the presence of the virus causing COVID-19 disease, namely SARS-CoV-2. Optical spectroscopy is a method that employs the interaction of light in detecting virus on samples. It is a promising method that might help in detecting the presence of SARS-CoV-2 in samples. Four optical spectroscopy methods are discussed in this paper: ultraviolet (UV), infrared (IR), Raman spectroscopy and fluorescence spectroscopy. UV and IR spectroscopy differ in wavelength range (less than 400 nm for UV, more than 700 nm for IR). Raman spectroscopy involves shift in wavelength due to scattering of light. Fluorescence spectroscopy involves difference in wavelength between absorbed and emitted light due to vibrational relaxation. These four methods had been proven to differentiate healthy samples from virus-infected samples. UV spectroscopy is useful in determining presence of virus based on 260 nm/280 nm absorbance ratio. However, its usefulness is limited due to its destructive properties on virus at sufficiently high intensity. Meanwhile, IR spectroscopy has becoming popular in studies involving virus samples. Mid-infrared (MIR) spectroscopy is most commonly used among IR spectroscopy as it usually provides useful information directly from spectral data. Near infrared (NIR) spectroscopy is also used in studying virus samples, but additional methods such as principal component analysis (PCA) and partial least squares (PLS) are required to process raw spectral data and to identify molecules based on spectral peaks. On the other hand, Raman spectroscopy is useful because spectral data can be analyzed directly in identifying vibrational modes of specific molecules in virus samples. Fluorescence spectroscopy relies on interaction between viral particles and fluorescent tags for the detection of virus based on improvement or quenching of fluorescent signal. Due to non-invasive properties of virus samples, IR, Raman and fluorescence spectroscopy will be used more often in future studies involving virus detection in infected samples.
ABSTRACT
The recent developments of deep learning support the identification and classification of lung diseases in medical images. Hence, numerous work on the detection of lung disease using deep learning can be found in the literature. This paper presents a survey of deep learning for lung disease detection in medical images. There has only been one survey paper published in the last five years regarding deep learning directed at lung diseases detection. However, their survey is lacking in the presentation of taxonomy and analysis of the trend of recent work. The objectives of this paper are to present a taxonomy of the state-of-the-art deep learning based lung disease detection systems, visualise the trends of recent work on the domain and identify the remaining issues and potential future directions in this domain. Ninety-eight articles published from 2016 to 2020 were considered in this survey. The taxonomy consists of seven attributes that are common in the surveyed articles: image types, features, data augmentation, types of deep learning algorithms, transfer learning, the ensemble of classifiers and types of lung diseases. The presented taxonomy could be used by other researchers to plan their research contributions and activities. The potential future direction suggested could further improve the efficiency and increase the number of deep learning aided lung disease detection applications.
