scholarly journals Advanced Object Detection in Bio-Medical X-Ray Images for Anomaly Detection and Recognition

2021 ◽  
Vol 12 (2) ◽  
pp. 93-110
Author(s):  
Garv Modwel ◽  
Anu Mehra ◽  
Nitin Rakesh ◽  
K. K. Mishra
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Vision System ◽  
Human Vision ◽  
Biomedical Image Processing ◽  
X Ray ◽  
Biomedical Image ◽  
X Ray Imaging ◽  
Magnetic Resonance Imaging Mri ◽  
Result Analysis

The human vision system is mimicked in the format of videos and images in the area of computer vision. As humans can process their memories, likewise video and images can be processed and perceptive with the help of computer vision technology. There is a broad range of fields that have great speculation and concepts building in the area of application of computer vision, which includes automobile, biomedical, space research, etc. The case study in this manuscript enlightens one about the innovation and future scope possibilities that can start a new era in the biomedical image-processing sector. A pre-surgical investigation can be perused with the help of the proposed technology that will enable the doctors to analyses the situations with deeper insight. There are different types of biomedical imaging such as magnetic resonance imaging (MRI), computerized tomographic (CT) scan, x-ray imaging. The focused arena of the proposed research is x-ray imaging in this subset. As it is always error-prone to do an eyeball check for a human when it comes to the detailing. The same applied to doctors. Subsequently, they need different equipment for related technologies. The methodology proposed in this manuscript analyses the details that may be missed by an expert doctor. The input to the algorithm is the image in the format of x-ray imaging; eventually, the output of the process is a label on the corresponding objects in the test image. The tool used in the process also mimics the human brain neuron system. The proposed method uses a convolutional neural network to decide on the labels on the objects for which it interprets the image. After some pre-processing the x-ray images, the neural network receives the input to achieve an efficient performance. The result analysis is done that gives a considerable performance in terms of confusion factor that is represented in terms of percentage. At the end of the narration of the manuscript, future possibilities are being traces out to the limelight to conduct further research.

scholarly journals Truncated Inception Net: COVID-19 Outbreak Screening using Chest X-rays

2020 ◽  
Author(s):  
Dipayan Das ◽  
KC Santosh ◽  
Umapada Pal
Keyword(s):  
Neural Network ◽  
World Wide ◽  
Imaging Techniques ◽  
Imaging Systems ◽  
Ct Scans ◽  
X Rays ◽  
X Ray ◽  
X Ray Imaging ◽  
Proposed Model ◽  
Different Types

Abstract Since December 2019, the Coronavirus Disease (COVID-19) pandemic has caused world-wide turmoil in less than a couple of months, and the infection, caused by SARS-CoV-2, is spreading at an unprecedented rate. AI-driven tools are used to identify Coronavirus outbreaks as well as forecast their nature of spread, where imaging techniques are widely used, such as CT scans and chest X-rays (CXRs). In this paper, motivated by the fact that X-ray imaging systems are more prevalent and cheaper than CT scan systems, a deep learning-based Convolutional Neural Network (CNN) model, which we call Truncated Inception Net, is proposed to screen COVID-19 positive CXRs from other non-COVID and/or healthy cases. To validate our proposal, six different types of datasets were employed by taking the following CXRs: COVID-19 positive, Pneumonia positive, Tuberculosis positive, and healthy cases into account. The proposed model achieved an accuracy of 99.96% (AUC of 1.0) in classifying COVID- 19 positive cases from combined Pneumonia and healthy cases. Similarly, it achieved an accuracy of 99.92% (AUC of 0.99) in classifying COVID-19 positive cases from combined Pneumonia, Tuberculosis and healthy CXRs. To the best of our knowledge, as of now, the achieved results outperform the existing AI-driven tools for screening COVID-19 using CXRs.

Biomedical Image Processing Overview

2016 ◽  
pp. 1-12 ◽  
Author(s):  
Monia Mannai Mannai ◽  
Wahiba Ben Abdessalem Karâa
Keyword(s):  
Image Processing ◽  
Medical Imaging ◽  
Medical Image Processing ◽  
Main Process ◽  
Nuclear Medicine Imaging ◽  
Biomedical Image Processing ◽  
Biomedical Image ◽  
X Ray Computed ◽  
Magnetic Resonance Imaging Mri ◽  
High Level

Over the years, there are different sorts of medical imaging have been developed. Where the most known are: X-ray, computed tomography (CT), nuclear medicine imaging (PET, SPECT), ultrasound and magnetic resonance imaging (MRI), each one has its different utilities. Various studies in biomedical informatics present a process to analyze images for extracting the hidden information which can be used after that. Image analysis combines several fields that are classified into two categories; the process of low-level, that requires very little information about the content image and the process of high-level, which may need information about the image content. The topic of this chapter is to introduce the different techniques for medical image processing and to present many research studies in this domain. It includes four stages, firstly, we introduce the most important medical imaging modalities and secondly, we outline the main process of biomedical image.

A SURVEY OF BIOLOGICALLY INSPIRED IMAGE PROCESSING FOR OBJECTS RECOGNITION

2009 ◽  
Vol 09 (04) ◽  
pp. 495-510 ◽  
Author(s):  
WEIREN SHI ◽  
ZUOJIN LI ◽  
XIN SHI ◽  
ZHI ZHONG
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Vision System ◽  
Human Vision ◽  
Image Perception ◽  
Biologically Inspired ◽  
Recognition Mechanism ◽  
Environmental Objects ◽  
Future Work ◽  
Animal Vision

The human vision system is a very sophisticated image processing and objects recognition mechanism. However, it is a challenge to simulate the human or animal vision system to automate visual function in machines, because it is difficult to account for the view-invariant perception of universals such as environmental objects or processes and the explicit perception of featural parts and wholes in visual scenes. In this paper, we first present an introduction to the importance of biologically inspired computer vision and review general and key vision functions from neuroscience perspective. And most significantly, we give an important summarization to and discussion on the specific applications of biologically inspired modeling, including biologically inspired image pre-processing, image perception, and objects recognition. In the end, we give some important and challenging topics of computer vision for future work.

SU-C-209-06: Improving X-Ray Imaging with Computer Vision and Augmented Reality

2016 ◽  
Vol 43 (6Part3) ◽  
pp. 3329-3329 ◽  
Author(s):  
R.D. MacDougall ◽  
S Don ◽  
B Scherrer
Keyword(s):  
Computer Vision ◽  
Augmented Reality ◽  
X Ray ◽  
X Ray Imaging

scholarly journals Analysis of the Functionality of the Feed Chain in Olive Pitting, Slicing and Stuffing Machines by IoT, Computer Vision and Neural Network Diagnosis

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1541
Author(s):  
Alberto Lucas Pascual ◽  
Antonio Madueño Luna ◽  
Manuel de Jódar Lázaro ◽  
José Miguel Molina Martínez ◽  
Antonio Ruiz Canales ◽  
...  
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Vision System ◽  
Operating Speed ◽  
Lighting System ◽  
Safety Issues ◽  
Optimal Functioning ◽  
Innovative Solution ◽  
Network Diagnosis ◽  
Small Parts

Olive pitting, slicing and stuffing machines (DRR in Spanish) are characterized by the fact that their optimal functioning is based on appropriate adjustments. Traditional systems are not completely reliable because their minimum error rate is 1–2%, which can result in fruit loss, since the pitting process is not infallible, and food safety issues can arise. Such minimum errors are impossible to remove through mechanical adjustments. In order to achieve this objective, an innovative solution must be provided in order to remove errors at operating speed rates over 2500 olives/min. This work analyzes the appropriate placement of olives in the pockets of the feed chain by using the following items: (1) An IoT System to control the DRR machine and the data analysis. (2) A computer vision system with an external shot camera and a LED lighting system, which takes a picture of every pocket passing in front of the camera. (3) A chip with a neural network for classification that, once trained, classifies between four possible pocket cases: empty, normal, incorrectly de-stoned olives at any angles (also known as a “boat”), and an anomalous case (foreign elements such as leafs, small branches or stones, two olives or small parts of olives in the same pocket). The main objective of this paper is to illustrate how with the use of a system based on IoT and a physical chip (NeuroMem CM1K, General Vision Inc.) with neural networks for sorting purposes, it is possible to optimize the functionality of this type of machine by remotely analyzing the data obtained. The use of classifying hardware allows it to work at the nominal operating speed for these machines. This would be limited if other classifying techniques based on software were used.

scholarly journals A Robust and Novel Approach for Brain Tumor Classification Using Convolutional Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Tahia Tazin ◽  
Sraboni Sarker ◽  
Punit Gupta ◽  
Fozayel Ibn Ayaz ◽  
Sumaia Islam ◽  
...  
Keyword(s):  
Neural Network ◽  
Brain Tumors ◽  
Convolutional Neural Network ◽  
Learning Strategy ◽  
Severe Form ◽  
Ultrasound Images ◽  
X Ray ◽  
Novel Approach ◽  
Magnetic Resonance Imaging Mri ◽  
Deep Feature Extraction

Brain tumors are the most common and aggressive illness, with a relatively short life expectancy in their most severe form. Thus, treatment planning is an important step in improving patients’ quality of life. In general, image methods such as computed tomography (CT), magnetic resonance imaging (MRI), and ultrasound images are used to assess tumors in the brain, lung, liver, breast, prostate, and so on. X-ray images, in particular, are utilized in this study to diagnose brain tumors. This paper describes the investigation of the convolutional neural network (CNN) to identify brain tumors from X-ray images. It expedites and increases the reliability of the treatment. Because there has been a significant amount of study in this field, the presented model focuses on boosting accuracy while using a transfer learning strategy. Python and Google Colab were utilized to perform this investigation. Deep feature extraction was accomplished with the help of pretrained deep CNN models, VGG19, InceptionV3, and MobileNetV2. The classification accuracy is used to assess the performance of this paper. MobileNetV2 had the accuracy of 92%, InceptionV3 had the accuracy of 91%, and VGG19 had the accuracy of 88%. MobileNetV2 has offered the highest level of accuracy among these networks. These precisions aid in the early identification of tumors before they produce physical adverse effects such as paralysis and other impairments.

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