scholarly journals Meta-Transfer Learning Driven Tensor-Shot Detector for the Autonomous Localization and Recognition of Concealed Baggage Threats

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6450
Author(s):  
Taimur Hassan ◽  
Muhammad Shafay ◽  
Samet Akçay ◽  
Salman Khan ◽  
Mohammed Bennamoun ◽  
...  
Keyword(s):  
Transfer Learning ◽  
State Of The Art ◽  
Autonomous Systems ◽  
Aviation Security ◽  
Dual Energy ◽  
Average Precision ◽  
X Ray ◽  
Shot Classification ◽  
Object Proposals ◽  
The World

Screening baggage against potential threats has become one of the prime aviation security concerns all over the world, where manual detection of prohibited items is a time-consuming and hectic process. Many researchers have developed autonomous systems to recognize baggage threats using security X-ray scans. However, all of these frameworks are vulnerable against screening cluttered and concealed contraband items. Furthermore, to the best of our knowledge, no framework possesses the capacity to recognize baggage threats across multiple scanner specifications without an explicit retraining process. To overcome this, we present a novel meta-transfer learning-driven tensor-shot detector that decomposes the candidate scan into dual-energy tensors and employs a meta-one-shot classification backbone to recognize and localize the cluttered baggage threats. In addition, the proposed detection framework can be well-generalized to multiple scanner specifications due to its capacity to generate object proposals from the unified tensor maps rather than diversified raw scans. We have rigorously evaluated the proposed tensor-shot detector on the publicly available SIXray and GDXray datasets (containing a cumulative of 1,067,381 grayscale and colored baggage X-ray scans). On the SIXray dataset, the proposed framework achieved a mean average precision (mAP) of 0.6457, and on the GDXray dataset, it achieved the precision and F1 score of 0.9441 and 0.9598, respectively. Furthermore, it outperforms state-of-the-art frameworks by 8.03% in terms of mAP, 1.49% in terms of precision, and 0.573% in terms of F1 on the SIXray and GDXray dataset, respectively.

scholarly journals Improving Coronavirus (COVID-19) Diagnosis using Deep Transfer Learning

2020 ◽  
Author(s):  
Arshia Rehman ◽  
Saeeda Naz ◽  
Ahmed Khan ◽  
Ahmad Zaib ◽  
Imran Razzak
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Bacterial Pneumonia ◽  
State Of The Art ◽  
High Accuracy ◽  
X Rays ◽  
Early Screening ◽  
X Ray ◽  
Learning Techniques ◽  
The World

AbstractBackgroundCoronavirus disease (COVID-19) is an infectious disease caused by a new virus. Exponential growth is not only threatening lives, but also impacting businesses and disrupting travel around the world.AimThe aim of this work is to develop an efficient diagnosis of COVID-19 disease by differentiating it from viral pneumonia, bacterial pneumonia and healthy cases using deep learning techniques.MethodIn this work, we have used pre-trained knowledge to improve the diagnostic performance using transfer learning techniques and compared the performance different CNN architectures.ResultsEvaluation results using K-fold (10) showed that we have achieved state of the art performance with overall accuracy of 98.75% on the perspective of CT and X-ray cases as a whole.ConclusionQuantitative evaluation showed high accuracy for automatic diagnosis of COVID-19. Pre-trained deep learning models develop in this study could be used early screening of coronavirus, however it calls for extensive need to CT or X-rays dataset to develop a reliable application.

The Average Precision Error of Dual Energy X-Ray Absorptiometry Performed by Multiple Bone Densitometry Technologists

2010 ◽  
Vol 13 (1) ◽  
pp. 136-137
Author(s):  
Yi-Shi Hwua ◽  
Iuan-Hong Tzeng ◽  
Yu-Ling LiRadiographer ◽  
Yuan-Chi LiuRadiographer ◽  
Sien-Huei LiaoRadiographer ◽  
...  
Keyword(s):  
Bone Densitometry ◽  
Dual Energy ◽  
Average Precision ◽  
Precision Error ◽  
X Ray

scholarly journals CovidMulti-Net: A Parallel-Dilated Multi Scale Feature Fusion Architecture for the Identification of COVID-19 Cases from Chest X-ray Images

2021 ◽  
Author(s):  
Md. Saikat Islam Khan ◽  
Anichur Rahman ◽  
Md. Razaul Karim ◽  
Nasima Islam Bithi ◽  
Shahab Band ◽  
...  
Keyword(s):  
Healthcare Professionals ◽  
Feature Fusion ◽  
State Of The Art ◽  
Research Community ◽  
Diagnostic Model ◽  
X Ray ◽  
Multi Scale ◽  
The World ◽  
Chest X Ray ◽  
Learning Concept

The COVID-19 pandemic is an emerging respiratory infectious disease, having a significant impact on the health and life of many people around the world. Therefore, early identification of COVID-19 patients is the fastest way to restrain the spread of the pandemic. However, as the number of cases grows at an alarming pace, most developing countries are now facing a shortage of medical resources and testing kits. Besides, using testing kits to detect COVID-19 cases is a time-consuming, expensive, and cumbersome procedure. Faced with these obstacles, most physicians, researchers, and engineers have advocated for the advancement of computer-aided deep learning models to assist healthcare professionals in quickly and inexpensively recognize COVID-19 cases from chest X-ray (CXR) images. With this motivation, this paper proposes a CovidMulti-Net architecture based on the transfer learning concept to classify COVID-19 cases from normal and other pneumonia cases using three publicly available datasets that include 1341, 1341, and 446 CXR images from healthy samples and 902, 1564, and 1193 CXR images infected with Viral Pneumonia, Bacterial Pneumonia, and COVID-19 diseases. In the proposed framework, features from CXR images are extracted using three well-known pre-trained models, including DenseNet-169, ResNet-50, and VGG-19. The extracted features are then fed into a concatenate layer, making a robust hybrid model. The proposed framework achieved a classification accuracy of 99.4%, 95.2%, and 94.8% for 2-Class, 3-Class, and 4-Class datasets, exceeding all the other state-of-the-art models. These results suggest that the CovidMulti-Net frameworks ability to discriminate individuals with COVID-19 infection from healthy ones and provides the opportunity to be used as a diagnostic model in clinics and hospitals. We also made all the materials publicly accessible for the research community at: https://github.com/saikat15010/CovidMulti-Net-Architecture.git.

The Classification of Covid-19 cases through the Employment of Transfer Learning on X-ray images

Mekatronika ◽  
2021 ◽  
Vol 3 (1) ◽  
pp. 44-51
Author(s):  
Nur Ameerah Hakimi ◽  
Mohd Azhar Mohd Razman ◽  
Anwar P. P. Abdul Majeed
Keyword(s):  
Transfer Learning ◽  
Screening Method ◽  
X Ray ◽  
The People ◽  
Machine Learning Model ◽  
The World ◽  
Chest X Ray ◽  
Alternative Test ◽  
Chest Radiology

Covid-19 is a contagious disease that known to cause respirotary infection in humans. Almost 219 countries are effected by the outbreak of the latest coronavirus pandemic, exceed 100 millions of confirmed cases and about 2 million death recorded aound the world. This condition is alarming as some of the people who are infected with the virus show no symptoms of the disease. Due to the number of confirmed cases rapidly rising around the world, it is crucial  find another method to diagnose the disease at the beginnings stage in order to control the spreading of the virus. Another alternative test from the main screening method is by using chest radiology image based detection which are X-ray or CT scan images. The aim of this research is to classify the Covid-19 cases by using the image classification technique.The dataset consist of 2000 images of chest X-ray images and have two classes which are Covid and Non-Covid. Each of the class consists of 1000 images.This research compare the performance of the various Transfer Learning models (VGG-16, VGG-19, and Inception V3) in extracting the feature from X-ray image combined with machine learning model (SVM, kNN, and Random Forest) as a classifier. The experiment result showed the VGG-19, VGG-16, and Inception V3 coupled with optimized SVM pipelines are comparably efficient in classifying the cases as compared to other pipelines evaluated in this reaseach and could archieved 99% acuuracy on the test datasets.

scholarly journals A Transfer Learning Method for Pneumonia Classification and Visualization

2020 ◽  
Vol 10 (8) ◽  
pp. 2908 ◽  
Author(s):  
Juan Luján-García ◽  
Cornelio Yáñez-Márquez ◽  
Yenny Villuendas-Rey ◽  
Oscar Camacho-Nieto
Keyword(s):  
Transfer Learning ◽  
Causes Of Death ◽  
State Of The Art ◽  
Machine Learning Algorithms ◽  
Learning Method ◽  
X Ray ◽  
Chest X Ray ◽  
Aided Diagnosis ◽  
Positive Results

Pneumonia is an infectious disease that affects the lungs and is one of the principal causes of death in children under five years old. The Chest X-ray images technique is one of the most used for diagnosing pneumonia. Several Machine Learning algorithms have been successfully used in order to provide computer-aided diagnosis by automatic classification of medical images. For its remarkable results, the Convolutional Neural Networks (models based on Deep Learning) that are widely used in Computer Vision tasks, such as classification of injuries and brain abnormalities, among others, stand out. In this paper, we present a transfer learning method that automatically classifies between 3883 chest X-ray images characterized as depicting pneumonia and 1349 labeled as normal. The proposed method uses the Xception Network pre-trained weights on ImageNet as an initialization. Our model is competitive with respect to state-of-the-art proposals. To make comparisons with other models, we have used four well-known performance measures, obtaining the following results: precision (0.84), recall (0.99), F1-score (0.91) and area under the ROC curve (0.97). These positive results allow us to consider our proposal as an alternative that can be useful in countries with a lack of equipment and specialized radiologists.

scholarly journals Fuzzification Based Osteoporosis Prediction Model

2021 ◽  
Vol 10 (6) ◽  
pp. 28-31
Author(s):  
Shilpa Kumar ◽  
Shubangi D C
Keyword(s):  
Quality Of Life ◽  
Risk Factors ◽  
Fuzzy Logic ◽  
Fuzzy Inference ◽  
Dual Energy ◽  
Literature Survey ◽  
Inference System ◽  
X Ray ◽  
The World

Osteoporosis is a disease in which bones become fragile and more likely to break. Osteoporosis can progress painlessly until it causes a bone fracture or a bone break. Dual Energy X-ray Absorptiometry (DEXA) is more costly and not accessible easily so we are using Fuzzy Inference system to predict osteoporosis. In this fuzzy logic, we collect risk factors and rules for osteoporosis and build a interface which take inputs and predicts if a person has osteoporosis. In the following Literature survey, we will take risk factors, rules, and ways to implement them. Around the world, 33% of women and 20% men over the age of 50 will suffer a fracture caused by Osteoporosis. Osteoporosis is a disease in which Bones become shallow and are fractured. If predicted before, quality of life will increase and severe surgery may be avoided.

scholarly journals Detection of Covid-19 with X-ray Images using Deep and Machine Learning

2021 ◽  
pp. 596-599
Author(s):  
Prof. Dr. Rajalakshmi M C ◽  
Bhuvana Sahi M ◽  
Bindu P ◽  
Pavan Kumar N ◽  
Pramod Athrey A
Keyword(s):  
Transfer Learning ◽  
Medical Image ◽  
State Of The Art ◽  
Medical Community ◽  
Network Architectures ◽  
X Rays ◽  
Community Based ◽  
X Ray ◽  
X Ray Imaging ◽  
Medical Image Classification

In this study, a dataset of X-ray images from patients with confirmed Covid -19 disease, and normal incidents, was utilized for the automatic detection of the Coronavirus disease. The aim of the study is to evaluate the performance of state-of-the-art convolutional neural network architectures proposed over the recent years for medical image classification. Specifically, the procedure called Transfer Learning was adopted. With transfer learning, the detection of various abnormalities in small medical image datasets is an achievable target, often yielding remarkable results. The datasets utilized in this experiment are Firstly, a collection of 5222 X-ray images including 3875 images with confirmed Covid -19 disease and 1347 images of normal conditions. The data was collected from the available X-ray images on public medical repositories. The results suggest that Deep Learning with X-ray imaging may extract significant biomarkers related to the Covid -19 disease, while the best accuracy, sensitivity, and specificity obtained is 96.78%, 98.66%, and 96.46% respectively. Since by now, all diagnostic tests show failure rates such as to raise concerns, the probability of incorporating X -rays into the diagnosis of the disease could be assessed by the medical community, based on the findings, while more research to evaluate the X-ray approach from different aspects may be conducted.

scholarly journals Dual-energy computed tomography in acute ischemic stroke: state-of-the-art

2020 ◽  
Author(s):  
Stephanie Mangesius ◽  
Tanja Janjic ◽  
Ruth Steiger ◽  
Lukas Haider ◽  
Rafael Rehwald ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Ischemic Stroke ◽  
Patient Management ◽  
State Of The Art ◽  
Dual Energy ◽  
Clinical State ◽  
X Ray ◽  
Dual Energy Computed Tomography ◽  
Future Developments ◽  
Potential Impact

Abstract Dual-energy computed tomography (DECT) allows distinguishing between tissues with similar X-ray attenuation but different atomic numbers. Recent studies demonstrated that this technique has several areas of application in patients with ischemic stroke and a potential impact on patient management. After endovascular stroke therapy (EST), hyperdense areas can represent either hemorrhage or contrast staining due to blood-brain barrier disruption, which can be differentiated reliably by DECT. Further applications are improved visualization of early infarctions, compared to single-energy computed tomography, and prediction of transformation into infarction or hemorrhage in contrast-enhancing areas. In addition, DECT allows detection and evaluation of the material composition of intra-arterial clots after EST. This review summarizes the clinical state-of-the-art of DECT in patients with stroke, and features some prospects for future developments. Key points • Dual-energy computed tomography (DECT) allows differentiation between tissues with similar X-ray attenuation but differentatomic numbers. • DECT has several areas of application in patients with ischemic stroke and a potential impact on patient management. • Prospects for future developments in DECT may improve treatment decision-making.

scholarly journals Within the Lack of Chest COVID-19 X-ray Dataset: A Novel Detection Model Based on GAN and Deep Transfer Learning

Symmetry ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 651 ◽  
Author(s):  
Mohamed Loey ◽  
Florentin Smarandache ◽  
Nour Eldeen M. Khalifa
Keyword(s):  
Transfer Learning ◽  
Healthcare Systems ◽  
World Health ◽  
Transfer Model ◽  
X Rays ◽  
Computer Algorithms ◽  
X Ray ◽  
The Third ◽  
The World ◽  
Testing Accuracy

The coronavirus (COVID-19) pandemic is putting healthcare systems across the world under unprecedented and increasing pressure according to the World Health Organization (WHO). With the advances in computer algorithms and especially Artificial Intelligence, the detection of this type of virus in the early stages will help in fast recovery and help in releasing the pressure off healthcare systems. In this paper, a GAN with deep transfer learning for coronavirus detection in chest X-ray images is presented. The lack of datasets for COVID-19 especially in chest X-rays images is the main motivation of this scientific study. The main idea is to collect all the possible images for COVID-19 that exists until the writing of this research and use the GAN network to generate more images to help in the detection of this virus from the available X-rays images with the highest accuracy possible. The dataset used in this research was collected from different sources and it is available for researchers to download and use it. The number of images in the collected dataset is 307 images for four different types of classes. The classes are the COVID-19, normal, pneumonia bacterial, and pneumonia virus. Three deep transfer models are selected in this research for investigation. The models are the Alexnet, Googlenet, and Restnet18. Those models are selected for investigation through this research as it contains a small number of layers on their architectures, this will result in reducing the complexity, the consumed memory and the execution time for the proposed model. Three case scenarios are tested through the paper, the first scenario includes four classes from the dataset, while the second scenario includes 3 classes and the third scenario includes two classes. All the scenarios include the COVID-19 class as it is the main target of this research to be detected. In the first scenario, the Googlenet is selected to be the main deep transfer model as it achieves 80.6% in testing accuracy. In the second scenario, the Alexnet is selected to be the main deep transfer model as it achieves 85.2% in testing accuracy, while in the third scenario which includes two classes (COVID-19, and normal), Googlenet is selected to be the main deep transfer model as it achieves 100% in testing accuracy and 99.9% in the validation accuracy. All the performance measurement strengthens the obtained results through the research.

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