Improvement on KNN using genetic algorithm and combined feature extraction to identify COVID-19 sufferers based on CT scan image

Coronavirus disease 2019 (COVID-19) has spread throughout the world. The detection of this disease is usually carried out using the reverse transcriptase polymerase chain reaction (RT-PCR) swab test. However, limited resources became an obstacle to carrying out the massive test. To solve this problem, computerized tomography (CT) scan images are used as one of the solutions to detect the sufferer. This technique has been used by researchers but mostly using classifiers that required high resources, such as convolutional neural network (CNN). In this study, we proposed a way to classify the CT scan images by using the more efficient classifier, k-nearest neighbors (KNN), for images that are processed using a combination of these feature extraction methods, Haralick, histogram, and local binary pattern. Genetic algorithm is also used for feature selection. The results showed that the proposed method was able to improve KNN performance, with the best accuracy of 93.30% for the combination of Haralick and local binary pattern feature extraction, and the best area under the curve (AUC) for the combination of Haralick, histogram, and local binary pattern with a value of 0.948. The best accuracy of our models also outperforms CNN by a 4.3% margin

SVM-based texture classification in optical coherence tomography

This paper describes a new method for automated texture classification for glaucoma detection using high resolution retinal Optical Coherence Tomography (OCT). OCT is a non-invasive technique that produces cross-sectional imagery of ocular tissue. Here, we exploit information from OCT im-ages, specifically the inner retinal layer thickness and speckle patterns, to detect glaucoma. The proposed method relies on support vector machines (SVM), while principal component analysis (PCA) is also employed to improve classification performance. Results show that texture features can improve classification accuracy over what is achieved using only layer thickness as existing methods currently do. Index Terms — classification, support vector machine, optical coherence tomography, texture 1

Roche tomography of the secondary stars in CVs

The secondary stars in cataclysmic variables (CVs) are key to our understanding of the origin, evolution and behaviour of this class of interacting binary. In seeking a fuller understanding of these objects, the challenge for observers is to obtain images of the secondary star. This goal can be achieved through Roche tomography, an indirect imaging technique that can be used to map the Roche-lobe-filling secondary. The review begins with a description of the basic principles that underpin Roche tomography, including methods for determining the system parameters. Finally, we conclude with a look at the main scientific highlights to date, including the first unambiguous detection of starspots on AE Aqr B, and consider the future prospects of this technique.Comment: 4 pages, 4 figures. Accepted for publication in A

Automatic Pulmonary Nodule Detection in CT Scans Using Convolutional Neural Networks Based on Maximum Intensity Projection

Accurate pulmonary nodule detection is a crucial step in lung cancer screening. Computer-aided detection (CAD) systems are not routinely used by radiologists for pulmonary nodule detection in clinical practice despite their potential benefits. Maximum intensity projection (MIP) images improve the detection of pulmonary nodules in radiological evaluation with computed tomography (CT) scans. Inspired by the clinical methodology of radiologists, we aim to explore the feasibility of applying MIP images to improve the effectiveness of automatic lung nodule detection using convolutional neural networks (CNNs). We propose a CNN-based approach that takes MIP images of different slab thicknesses (5 mm, 10 mm, 15 mm) and 1 mm axial section slices as input. Such an approach augments the two-dimensional (2-D) CT slice images with more representative spatial information that helps discriminate nodules from vessels through their morphologies. Our proposed method achieves sensitivity of 92.67% with 1 false positive per scan and sensitivity of 94.19% with 2 false positives per scan for lung nodule detection on 888 scans in the LIDC-IDRI dataset. The use of thick MIP images helps the detection of small pulmonary nodules (3 mm-10 mm) and results in fewer false positives. Experimental results show that utilizing MIP images can increase the sensitivity and lower the number of false positives, which demonstrates the effectiveness and significance of the proposed MIP-based CNNs framework for automatic pulmonary nodule detection in CT scans. The proposed method also shows the potential that CNNs could gain benefits for nodule detection by combining the clinical procedure.Comment: Submitted to IEEE TM

In-Situ Defect Detection in Laser Powder Bed Fusion by Using Thermography and Optical Tomography—Comparison to Computed Tomography

Among additive manufacturing (AM) technologies, the laser powder bed fusion (L-PBF) is one of the most important technologies to produce metallic components. The layer-wise build-up of components and the complex process conditions increase the probability of the occurrence of defects. However, due to the iterative nature of its manufacturing process and in contrast to conventional manufacturing technologies such as casting, L-PBF offers unique opportunities for in-situ monitoring. In this study, two cameras were successfully tested simultaneously as a machine manufacturer independent process monitoring setup: a high-frequency infrared camera and a camera for long time exposure, working in the visible and infrared spectrum and equipped with a near infrared filter. An AISI 316L stainless steel specimen with integrated artificial defects has been monitored during the build. The acquired camera data was compared to data obtained by computed tomography. A promising and easy to use examination method for data analysis was developed and correlations between measured signals and defects were identified. Moreover, sources of possible data misinterpretation were specified. Lastly, attempts for automatic data analysis by data integration are presented

Universal in vivo Textural Model for Human Skin based on Optical Coherence Tomograms

Currently, diagnosis of skin diseases is based primarily on visual pattern recognition skills and expertise of the physician observing the lesion. Even though dermatologists are trained to recognize patterns of morphology, it is still a subjective visual assessment. Tools for automated pattern recognition can provide objective information to support clinical decision-making. Noninvasive skin imaging techniques provide complementary information to the clinician. In recent years, optical coherence tomography has become a powerful skin imaging technique. According to specific functional needs, skin architecture varies across different parts of the body, as do the textural characteristics in OCT images. There is, therefore, a critical need to systematically analyze OCT images from different body sites, to identify their significant qualitative and quantitative differences. Sixty-three optical and textural features extracted from OCT images of healthy and diseased skin are analyzed and in conjunction with decision-theoretic approaches used to create computational models of the diseases. We demonstrate that these models provide objective information to the clinician to assist in the diagnosis of abnormalities of cutaneous microstructure, and hence, aid in the determination of treatment. Specifically, we demonstrate the performance of this methodology on differentiating basal cell carcinoma (BCC) and squamous cell carcinoma (SCC) from healthy tissue