Osteoporosis Prediction for Trabecular Bone using Machine Learning: A Review

Anam, Marrium; Ponnusamy, Vasaki; Hussain, Muzammil; Nadeem, Muhammad Waqas; Awan, Mazhar Javed; Goh, Hock Guan; Qadeer, Sadia

doi:10.32604/cmc.2021.013159

Cited by 60 publications

(28 citation statements)

References 58 publications

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“…The main intention of this investigation is to make a Deep Transfer Learning (DTL) structure utilizing Convolutional Neural Network (CNN) with the Apache Spark big data platform, in light of pre-arranged models InceptionV3, ResNet50, and VGG19. This work was inspired by deep-learning work in [ 37 , 38 , 39 , 40 ].…”

Section: Introductionmentioning

confidence: 99%

Detection of COVID-19 in Chest X-ray Images: A Big Data Enabled Deep Learning Approach

Awan

Bilal

Yasin

et al. 2021

IJERPH

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Coronavirus disease (COVID-19) spreads from one person to another rapidly. A recently discovered coronavirus causes it. COVID-19 has proven to be challenging to detect and cure at an early stage all over the world. Patients showing symptoms of COVID-19 are resulting in hospitals becoming overcrowded, which is becoming a significant challenge. Deep learning’s contribution to big data medical research has been enormously beneficial, offering new avenues and possibilities for illness diagnosis techniques. To counteract the COVID-19 outbreak, researchers must create a classifier distinguishing between positive and negative corona-positive X-ray pictures. In this paper, the Apache Spark system has been utilized as an extensive data framework and applied a Deep Transfer Learning (DTL) method using Convolutional Neural Network (CNN) three architectures —InceptionV3, ResNet50, and VGG19—on COVID-19 chest X-ray images. The three models are evaluated in two classes, COVID-19 and normal X-ray images, with 100 percent accuracy. But in COVID/Normal/pneumonia, detection accuracy was 97 percent for the inceptionV3 model, 98.55 percent for the ResNet50 Model, and 98.55 percent for the VGG19 model, respectively.

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Section: Introductionmentioning

confidence: 99%

Detection of COVID-19 in Chest X-ray Images: A Big Data Enabled Deep Learning Approach

Awan

Bilal

Yasin

et al. 2021

IJERPH

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“…e improved model enhanced the correlation between these two by adding an intersection over the union (IoU) prediction loss branch. Deep learning rose to its prominent position in 2 Complexity digital image processing and computer vision when neural networks were applied in various types of medical image analysis datasets [27,28]. Recently, an approach [29] has been published; in this study, the author develops an automated system for stomata detection, which can detect individual stomata boundaries regardless of the plant species.…”

Section: Related Workmentioning

confidence: 99%

Stomatal State Identification and Classification in Quinoa Microscopic Imprints through Deep Learning

et al. 2021

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Stomata are the main medium of plants for the trade of water, regulate the gas exchange, and are responsible for the process of photosynthesis and transpiration. The stomata are surrounded by guard cells, which help to control the rate of transpiration by opening and closing the stomata. The stomata states (open and close) play a significant role in describing the plant’s health. Moreover, stomata counting is important for scientists to investigate the numbers of stomata that are open and those that are closed to measure their density and distribution on the surface of leaves through different sampling techniques. Although a few techniques for stomata counting have been proposed, these approaches do not identify and classify the stomata based on their states in leaves. In this research, we have developed an automatic system for stomata state identification and counting in quinoa leaf images through the transformed learning (neural network model Single Shot Detector) approach. In leaf imprint, the state of stomata has been determined by measuring the correlation between the area of stomata and the aperture of each detected stoma in the image. The stomata states have been classified through the Support Vector Machine (SVM) algorithm. The overall identification and classification accuracy of the proposed system are 98.6% and 97%, respectively, helping researchers to obtain accurate stomatal state information for leaves in an efficient and simple way.

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“…Moving on from this way, the most cutting-edge platforms use image processing along with help of machine learning or deep learning-based approaches for the classification of malware. A common solution is to use the ML algorithms and artificial neural networks (ANNs), which can be combined into more complex architectures such as using ensemble learning to identify malware from the features extracted from malware characteristics [15][16][17][18][19][20][21]. A popular choice has been the use of NNs along with SVM which were the most popular technique for malware classification adversarial attacks.…”

Section: Introductionmentioning

confidence: 99%

Image-Based Malware Classification Using VGG19 Network and Spatial Convolutional Attention

et al. 2021

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In recent years the amount of malware spreading through the internet and infecting computers and other communication devices has tremendously increased. To date, countless techniques and methodologies have been proposed to detect and neutralize these malicious agents. However, as new and automated malware generation techniques emerge, a lot of malware continues to be produced, which can bypass some state-of-the-art malware detection methods. Therefore, there is a need for the classification and detection of these adversarial agents that can compromise the security of people, organizations, and countless other forms of digital assets. In this paper, we propose a spatial attention and convolutional neural network (SACNN) based on deep learning framework for image-based classification of 25 well-known malware families with and without class balancing. Performance was evaluated on the Malimg benchmark dataset using precision, recall, specificity, precision, and F1 score on which our proposed model with class balancing reached 97.42%, 97.95%, 97.33%, 97.11%, and 97.32%. We also conducted experiments on SACNN with class balancing on benign class, also produced above 97%. The results indicate that our proposed model can be used for image-based malware detection with high performance, despite being simpler as compared to other available solutions.

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Osteoporosis Prediction for Trabecular Bone using Machine Learning: A Review

Cited by 60 publications

References 58 publications

Detection of COVID-19 in Chest X-ray Images: A Big Data Enabled Deep Learning Approach

Detection of COVID-19 in Chest X-ray Images: A Big Data Enabled Deep Learning Approach

Stomatal State Identification and Classification in Quinoa Microscopic Imprints through Deep Learning

Image-Based Malware Classification Using VGG19 Network and Spatial Convolutional Attention

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