The rapid expansion of information science has caused the issue of “the curse of dimensionality”, which will negatively affect the performance of the machine learning model. Feature selection is typically considered as a pre-processing mechanism to find an optimal subset of features from a given set of all features in the data mining process. In this article, a novel Hyper Learning Binary Dragonfly Algorithm (HLBDA) is proposed as a wrapper-based method to find an optimal subset of features for a given classification problem. HLBDA is an enhanced version of the Binary Dragonfly Algorithm (BDA) in which a hyper learning strategy is used to assist the algorithm to escape local optima and improve searching behavior. The proposed HLBDA is compared with eight algorithms in the literature. Several assessment indicators are employed to evaluate and compare the effectiveness of these methods over twenty-one datasets from the University of California Irvine (UCI) repository and Arizona State University. Also, the proposed method is applied to a coronavirus disease (COVID-19) dataset. The results demonstrate the superiority of HLBDA in increasing classification accuracy and reducing the number of selected features. 2

A hyper learning binary dragonfly algorithm for feature selection: A COVID-19 case study

COVID-19 X-ray images classification based on enhanced fractional-order cuckoo search optimizer using heavy-tailed distributions

Ongoing improvements in AI, particularly concerning deep learning techniques, are assisting to identify, classify, and quantify patterns in clinical images. Deep learning is the quickest developing field in artificial intelligence and is effectively utilized lately in numerous areas, including medication. A brief outline is given on studies carried out on the region of application: neuro, brain, retinal, pneumonic, computerized pathology, bosom, heart, breast, bone, stomach, and musculoskeletal. For information exploration, knowledge deployment, and knowledge-based prediction, deep learning networks can be successfully applied to big data. In the field of medical image processing methods and analysis, fundamental information and state-of-the-art approaches with deep learning are presented in this paper. The primary goals of this paper are to present research on medical image processing as well as to define and implement the key guidelines that are identified and addressed.

/pdf/a-review-on-deep-learning-in-medical-image-analysis-27s3sw5i0e.pdf

A review on deep learning in medical image analysis.

Light microscopes were developed in the early 1600’s. By the end of the century Dutch microscopist van Leeuwenhoek had imaged blood cells, bacteria and structure within the cell using a simple one-lens microscope with x300 magnification which was awkward to use. At the same time the compound microscope was being developed. This uses at least two lenses; an objective, placed close to the specimen, and an eyepiece (ocular), placed close to the eye. Such was van Leeuwenhoek’s skill, however, that it was two centuries before the compound microscope could match his work. Compound optical microscopes, these days, have a maximum magnification of about x1000 and an imaging resolution of around 0.3μm for light in the middle of the visible region (λ~0.5μm). Aberrations in light optical lenses can be reduced to a minimum by grinding the lens surface to a correct shape or spacing the lenses so that their aberrations are compensated. In the absence of aberrations the limit of resolution (d) of any wave optical microscope is given by the Rayleigh criterion:-

Introduction to electron microscopy

Coronavirus disease, first detected in late 2019 (COVID-19), has spread fast throughout the world, leading to high mortality. This condition can be diagnosed using RT-PCR technique on nasopharyngeal and throat swabs with sensitivity values ranging from 30 to 70%. However, chest CT scans and X-ray images have been reported to have sensitivity values of 98 and 69%, respectively. The application of machine learning methods on CT and X-ray images has facilitated the accurate diagnosis of COVID-19. In this study, we reviewed studies which used machine and deep learning methods on chest X-ray images and CT scans for COVID-19 diagnosis and compared their performance. The accuracy of these methods ranged from 76% to more than 99%, indicating the applicability of machine and deep learning methods in the clinical diagnosis of COVID-19.

/pdf/application-of-machine-learning-in-diagnosis-of-covid-19-5gpdlj6j32.pdf

Daksh Thakur

Papers

A deep learning approach to detect Covid-19 coronavirus with X-Ray images.