Cargo segmentation in stream of commerce (SoC) X-ray images with deep learning algorithms

2020 ◽  
Author(s):  
Weicheng Shen ◽  
Jaroslaw Tuszynski
Keyword(s):  
Deep Learning ◽  
Learning Algorithms ◽  
X Ray

scholarly journals Certain Investigations on the Application of Machine learning Algorithms and Deep Learning Architectures for Covid -19 Diagnosis

2021 ◽  
Author(s):  
Soundariya R.S. ◽  
◽  
Tharsanee R.M. ◽  
Vishnupriya B ◽  
Ashwathi R ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Mathematical Models ◽  
Ct Scan ◽  
Virus Disease ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
X Ray ◽  
Laboratory Test Results ◽  
Learning Architectures

Corona virus disease (Covid - 19) has started to promptly spread worldwide from April 2020 till date, leading to massive death and loss of lives of people across various countries. In accordance to the advices of WHO, presently the diagnosis is implemented by Reverse Transcription Polymerase Chain Reaction (RT- PCR) testing, that incurs four to eight hours’ time to process test samples and adds 48 hours to categorize whether the samples are positive or negative. It is obvious that laboratory tests are time consuming and hence a speedy and prompt diagnosis of the disease is extremely needed. This can be attained through several Artificial Intelligence methodologies for prior diagnosis and tracing of corona diagnosis. Those methodologies are summarized into three categories: (i) Predicting the pandemic spread using mathematical models (ii) Empirical analysis using machine learning models to forecast the global corona transition by considering susceptible, infected and recovered rate. (iii) Utilizing deep learning architectures for corona diagnosis using the input data in the form of X-ray images and CT scan images. When X-ray and CT scan images are taken into account, supplementary data like medical signs, patient history and laboratory test results can also be considered while training the learning model and to advance the testing efficacy. Thus the proposed investigation summaries the several mathematical models, machine learning algorithms and deep learning frameworks that can be executed on the datasets to forecast the traces of COVID-19 and detect the risk factors of coronavirus.

On Data Bias and the Usability of Deep Learning Algorithms in Classifying COVID-19 based on Chest X-ray

2021 ◽  
Author(s):  
Hassane Ezzeddine ◽  
Mariette Awad ◽  
Alain S. Abi Ghanem ◽  
Bassem Mourani
Keyword(s):  
Deep Learning ◽  
Learning Algorithms ◽  
X Ray ◽  
Chest X Ray

scholarly journals Novel Privacy Preserving Non-Invasive Sensing-Based Diagnoses of Pneumonia Disease Leveraging Deep Network Model

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 461
Author(s):  
Mujeeb Ur Rehman ◽  
Arslan Shafique ◽  
Kashif Hesham Khan ◽  
Sohail Khalid ◽  
Abdullah Alhumaidi Alotaibi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Medical Records ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
X Ray ◽  
Non Invasive ◽  
Proposed Model ◽  
Pneumonia Diagnosis ◽  
Better Than

This article presents non-invasive sensing-based diagnoses of pneumonia disease, exploiting a deep learning model to make the technique non-invasive coupled with security preservation. Sensing and securing healthcare and medical images such as X-rays that can be used to diagnose viral diseases such as pneumonia is a challenging task for researchers. In the past few years, patients’ medical records have been shared using various wireless technologies. The wireless transmitted data are prone to attacks, resulting in the misuse of patients’ medical records. Therefore, it is important to secure medical data, which are in the form of images. The proposed work is divided into two sections: in the first section, primary data in the form of images are encrypted using the proposed technique based on chaos and convolution neural network. Furthermore, multiple chaotic maps are incorporated to create a random number generator, and the generated random sequence is used for pixel permutation and substitution. In the second part of the proposed work, a new technique for pneumonia diagnosis using deep learning, in which X-ray images are used as a dataset, is proposed. Several physiological features such as cough, fever, chest pain, flu, low energy, sweating, shaking, chills, shortness of breath, fatigue, loss of appetite, and headache and statistical features such as entropy, correlation, contrast dissimilarity, etc., are extracted from the X-ray images for the pneumonia diagnosis. Moreover, machine learning algorithms such as support vector machines, decision trees, random forests, and naive Bayes are also implemented for the proposed model and compared with the proposed CNN-based model. Furthermore, to improve the CNN-based proposed model, transfer learning and fine tuning are also incorporated. It is found that CNN performs better than other machine learning algorithms as the accuracy of the proposed work when using naive Bayes and CNN is 89% and 97%, respectively, which is also greater than the average accuracy of the existing schemes, which is 90%. Further, K-fold analysis and voting techniques are also incorporated to improve the accuracy of the proposed model. Different metrics such as entropy, correlation, contrast, and energy are used to gauge the performance of the proposed encryption technology, while precision, recall, F1 score, and support are used to evaluate the effectiveness of the proposed machine learning-based model for pneumonia diagnosis. The entropy and correlation of the proposed work are 7.999 and 0.0001, respectively, which reflects that the proposed encryption algorithm offers a higher security of the digital data. Moreover, a detailed comparison with the existing work is also made and reveals that both the proposed models work better than the existing work.

scholarly journals Using Machine Learning via Deep Learning Algorithms to Diagnose the Lung Disease Based on Chest Imaging: A Survey

2021 ◽  
Vol 15 (16) ◽  
pp. 95
Author(s):  
Shaymaa Taha Ahmed ◽  
Suhad Malallah Kadhem
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Lung Disease ◽  
Lung Diseases ◽  
Learning Algorithms ◽  
Learning Approaches ◽  
Chest Imaging ◽  
Training Set ◽  
X Ray ◽  
State Of Art

<p class="0abstract"><strong>—</strong> Chest imaging diagnostics is crucial in the medical area due to many serious lung diseases like cancers and nodules and particularly with the current pandemic of Covid-19. Machine learning approaches yield prominent results toward the task of diagnosis. Recently, deep learning methods are utilized and recommended by many studies in this domain. The research aims to critically examine the newest lung disease detection procedures using deep learning algorithms that use X-ray and CT scan datasets. Here, the most recent studies in this area (2015-2021) have been reviewed and summarized to provide an overview of the most appropriate methods that should be used or developed in future works, what limitations should be considered, and at what level these techniques help physicians in identifying the disease with better accuracy. The lack of various standard datasets, the huge training set, the high dimensionality of data, and the independence of features have been the main limitations based on the literature. However, different architectures of deep learning are used by many researchers but, Convolutional Neural Networks (CNN) are still state-of-art techniques in dealing with image datasets.</p>

scholarly journals The Effectiveness of Image Augmentation in Deep Learning Networks for Detecting COVID-19: A Geometric Transformation Perspective

2021 ◽  
Vol 8 ◽  
Author(s):  
Mohamed Elgendi ◽  
Muhammad Umer Nasir ◽  
Qunfeng Tang ◽  
David Smith ◽  
John-Paul Grenier ◽  
...  
Keyword(s):  
Deep Learning ◽  
Data Augmentation ◽  
Learning Algorithms ◽  
Training Dataset ◽  
Geometric Transformation ◽  
Detection Accuracy ◽  
X Rays ◽  
X Ray ◽  
Testing Dataset ◽  
The Impact

Chest X-ray imaging technology used for the early detection and screening of COVID-19 pneumonia is both accessible worldwide and affordable compared to other non-invasive technologies. Additionally, deep learning methods have recently shown remarkable results in detecting COVID-19 on chest X-rays, making it a promising screening technology for COVID-19. Deep learning relies on a large amount of data to avoid overfitting. While overfitting can result in perfect modeling on the original training dataset, on a new testing dataset it can fail to achieve high accuracy. In the image processing field, an image augmentation step (i.e., adding more training data) is often used to reduce overfitting on the training dataset, and improve prediction accuracy on the testing dataset. In this paper, we examined the impact of geometric augmentations as implemented in several recent publications for detecting COVID-19. We compared the performance of 17 deep learning algorithms with and without different geometric augmentations. We empirically examined the influence of augmentation with respect to detection accuracy, dataset diversity, augmentation methodology, and network size. Contrary to expectation, our results show that the removal of recently used geometrical augmentation steps actually improved the Matthews correlation coefficient (MCC) of 17 models. The MCC without augmentation (MCC = 0.51) outperformed four recent geometrical augmentations (MCC = 0.47 for Data Augmentation 1, MCC = 0.44 for Data Augmentation 2, MCC = 0.48 for Data Augmentation 3, and MCC = 0.49 for Data Augmentation 4). When we retrained a recently published deep learning without augmentation on the same dataset, the detection accuracy significantly increased, with a χMcNemar′s statistic2=163.2 and a p-value of 2.23 × 10−37. This is an interesting finding that may improve current deep learning algorithms using geometrical augmentations for detecting COVID-19. We also provide clinical perspectives on geometric augmentation to consider regarding the development of a robust COVID-19 X-ray-based detector.

scholarly journals Diagnosing COVID-19 pneumonia from x-ray and CT images using deep learning and transfer learning algorithms

2021 ◽  
Author(s):  
Halgurd Maghdid ◽  
Aras T. Asaad ◽  
Kayhan Zrar Ghafoor Ghafoor ◽  
Ali S. Sadiq ◽  
Seyedali Mirjalili ◽  
...  
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Learning Algorithms ◽  
Ct Images ◽  
X Ray

Developing an Artificial Intelligence Project in your Radiology Department

2020 ◽  
Vol 2 ◽  
pp. 58-61 ◽  
Author(s):  
Syed Junaid ◽  
Asad Saeed ◽  
Zeili Yang ◽  
Thomas Micic ◽  
Rajesh Botchu
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Health Care ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Radiology Department ◽  
Short Article ◽  
Computing Power ◽  
Road Map ◽  
Key Concepts

The advances in deep learning algorithms, exponential computing power, and availability of digital patient data like never before have led to the wave of interest and investment in artificial intelligence in health care. No radiology conference is complete without a substantial dedication to AI. Many radiology departments are keen to get involved but are unsure of where and how to begin. This short article provides a simple road map to aid departments to get involved with the technology, demystify key concepts, and pique an interest in the field. We have broken down the journey into seven steps; problem, team, data, kit, neural network, validation, and governance.

Sign in / Sign up

Export Citation Format

Share Document