Enhanced feature selection algorithm for pneumonia detection

Pneumonia is a type of lung disease that can be detected using X-ray images. The analysis of chest X-ray images is an active research area in medical image analysis and computer-aided radiology. This research aims to improve the accuracy and efficiency of radiologists' work by providing a technique for identifying and categorizing diseases. More attention should be given to applying machine learning approaches to develop a robust chest X-ray image classification method. The typical method for detecting Pneumonia is through chest X-ray images, but analyzing these images can be complex and requires the expertise of a radiographer. This paper demonstrates the feasibility of detecting the disease using chest X-ray images as datasets and a Support Vector Machine combined with a Naive Bayesian classifier, with PCA and GA as feature selection methods. The selected features are essential for training many classifiers. The proposed system achieved an accuracy of 92.26%, using 91% of the principal component. The study's result suggests that using PCA and GA for feature selection in chest X-ray image classification can achieve a good accuracy of 97.44%. Further research is needed to explore the use of other data mining models and care components to improve the accuracy and effectiveness of the system

CLASSIFICATION OF THORAX DISEASES FROM CHEST X-RAY IMAGES

Chest X-ray images are crucial for medical decisions and patient care. However, their manual interpretation is time-consuming and prone to human error. This project aims to create an automated system that uses deep learning techniques to classify thorax disease from chest X-ray images. We are using the NIH Chest X-Ray Dataset, which contains many annotated images, as input data for this project. This approach uses UNet architecture as its classification layer. UNet architecture is well-known for its efficiency in image segmentation. Adding residual blocks enhances this approach\u27s ability to classify images. The goal of this project is to create a robust and accurate classification model that uses UNet’s unique capabilities for feature representation and extraction. This would allow accurate discrimination between different forms of thorax diseases with high precision. This project shows the effectiveness of UNet architecture with residual block for accurately classifying thorax disease types. These techniques combined produced superior results to many other architectures for medical image analysis, underscoring their importance

High Resolution X-Ray CT for Advanced Electronics Packaging

Advanced electronics packaging is a challenge for non-destructive Testing (NDT). More, smaller and mostly hidden interconnects dominate modern electronics components and systems. To solve the demands of customers to get products with a high functionality by low volume, weight and price (e.g. mobile phones, personal medical monitoring systems) often the designers use System-in-Package solutions (SiP). The non-destructive testing of such devices is a big challenge (see [1]). So our paper will impart fundamentals and applications for non-destructive evaluation of inner structures of electronics packaging for quality assurance and reliability investigations. The main NDE methods for electronics packaging are scanning acoustic microscopy and X-ray macrostructure analysis like X-ray radiography and X-ray computed tomography (CT) (see [2] & [3]). Our presentation will focus on X-ray nano focus computed tomography as a method for component development, process development and reliability research. We will discuss the potentials and the limits of X-ray NDE techniques, illustrated by crack observation in solder joints, evaluation of micro vias in PCBs and interposers and the investigation of a complex SiP like a USB memory device. We will show tomography results with voxel sizes less than 800nm. To reach these results we developed special techniques to prepare the samples for high resolution CTs. Figure 1 shows the tool, a prepared specimen and a high resolution CT result picture

Quantum optimization algorithms for CT image segmentation from X-ray data

Computed tomography (CT) is an important imaging technique used in medical analysis of the internal structure of the human body. Previously, image segmentation methods were required after acquiring reconstructed CT images to obtain segmented CT images which made it susceptible to errors from both reconstruction and segmentation algorithms. However, this paper introduces a new approach using an advanced quantum optimization algorithm called quadratic unconstrained binary optimization (QUBO). This algorithm enables acquisition of segmented CT images from X-ray projection data with minimized discrepancies between experimentally obtained sinograms and quantized sinograms derived from quantized segmented CT images using the Radon transform. This study utilized D-Wave's hybrid solver system for verification on real-world X-ray data.Comment: 7 Pages, 3 figure

Detection and prediction of osteoarthritis in knee and hand joints based on the X-ray image analysis

Current assessment of osteoarthritis (OA) is primary based on visual grading of joint space narrowing and osteophytes present on radiographs. The approach is observer-dependent, not sensitive enough for the detection of the early stages of OA and time consuming. A promising solution is through fractal analysis of trabecular bone (TB) textures on radiographs. The goal is to develop an automated decision support system for the detection and prediction of OA based on TB texture regions selected on knee and hand radiographs. In this review, we describe our progress towards this development which was conducted in five stages, i.e., (i) development of automated methods for the selection of TB texture regions on knee and hand radiographs (ii), development of fractal signature methods for TB texture analysis, (iii) applications of the methods in the analysis of x-ray images of knees and hands, (iv) development of TB texture classification system, and (v) development of ReadMyXray website for knee x-ray analysis. The results achieved so far are encouraging and it is hoped, that once the system is fully developed and evaluated, it will be used to aid medical practitioners in the decision-making, i.e., in designing OA preventative measures, treatments and monitoring the OA progression

Chest X-Rays Image Classification in Medical Image Analysis

Chest X-Rays image classification is an active research area in medical image analysis as well as computer-aided diagnosis for radiology. The main goal is to improve the quality and productivity of radiologists’ task by providing a computer system for detecting and classifying diseases. A few studies have been conducted in applying machine learning methods to produce a high-quality chest X-ray image classification approach. Some review papers have been published in discussing different aspects of medical image analysis and computer-aided diagnosis for radiology. This paper aims to complement the existing surveys by targeting on the chest X-ray image classification approaches base on the use of machine learning methods. The review begins with a background information of data mining, and the fundamental knowledge of medical image analysis, chest radiography, and machine learning

Invariant Scattering Transform for Medical Imaging

Invariant scattering transform introduces new area of research that merges the signal processing with deep learning for computer vision. Nowadays, Deep Learning algorithms are able to solve a variety of problems in medical sector. Medical images are used to detect diseases brain cancer or tumor, Alzheimer's disease, breast cancer, Parkinson's disease and many others. During pandemic back in 2020, machine learning and deep learning has played a critical role to detect COVID-19 which included mutation analysis, prediction, diagnosis and decision making. Medical images like X-ray, MRI known as magnetic resonance imaging, CT scans are used for detecting diseases. There is another method in deep learning for medical imaging which is scattering transform. It builds useful signal representation for image classification. It is a wavelet technique; which is impactful for medical image classification problems. This research article discusses scattering transform as the efficient system for medical image analysis where it's figured by scattering the signal information implemented in a deep convolutional network. A step by step case study is manifested at this research work.Comment: 11 pages, 8 figures and 1 tabl

Medical image analysis and visualization using geometric deformable model

Medical image analysis and visualization has become increasingly important in computer aided medicine. Throughout the history of medicine, advances in imaging have led to great progress in medical interventions. The thesis proposes, develops and evaluates methods for automated analysis, visualization and quantification of medical images. The focus of this thesis is to perform both theoretical and practical investigations into medical image analysis and visualization to overcome current challenges in the field. The theoretical framework for fulfilling above goals is based on segmentation using the geometric deformable model and some new advances: support vector machine and principal component analysis from the pattern recognition and machine learning. The medical applications of the above theoretical framework include automated computer aided analysis of dental X-ray image and chest computer tomography volumetric image reconstruction and visualization. There are three main contributions in the thesis: (1) We propose and develop two faster and more robust segmentation methods which have the potential to be used in clinical and hospital environments. (2) We propose and develop the first dental X-ray image analysis and visualization system. It is able to analyze the dental X-ray image, extract the features and then recognize the patterns of certain diseases such as root decay and areas of bone loss. It has potential to be applied in the dental X-ray machine which has attracted interest from industry. (3) We propose and develop an efficient reconstruction and visualization framework. This method can reconstruct and visualize very large medical datasets with less time and less data volum