Acceleration of Knee MRI Cancellous bone Classification on Google Colaboratory using Convolutional Neural Network

Awan, Mazhar Javed

doi:10.30534/ijatcse/2019/1381.62019

Cited by 28 publications

(17 citation statements)

References 18 publications

(20 reference statements)

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“…It is an analytics stage based on Apache Spark. It is a cloud-based platform similar to the Google Colab cloud environment [ 68 ]. Databricks is a synergistic platform that permits clients to concentrate the entirety of their logical tasks and oversee machine-learning models over as long as they can remember the cycle.…”

Section: Experiments and Resultsmentioning

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: Experiments and Resultsmentioning

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

Self Cite

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“…We used Google Colab Cloud for our experiments using Python Language 3.5, Tensorflow, Scikit learn, and the Keras library. The labels were inferred from the dataset by TensorFlow Keras's function called image_dataset_from_directory [66].…”

Section: Resultsmentioning

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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“…The experiments were carried out on Google Colab with Python 3.6. The paper [ 59 ] in which the CNN model was implemented on knee cancellous bones achieved 99% accuracy, with better acceleration. So we selected Google Colab, providing free GPU, with the specifications of the Tesla K80 processor having 2496 CUDA cores and 12GB ram.…”

Section: Resultsmentioning

confidence: 99%

Efficient Detection of Knee Anterior Cruciate Ligament from Magnetic Resonance Imaging Using Deep Learning Approach

et al. 2021

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The most commonly injured ligament in the human body is an anterior cruciate ligament (ACL). ACL injury is standard among the football, basketball and soccer players. The study aims to detect anterior cruciate ligament injury in an early stage via efficient and thorough automatic magnetic resonance imaging without involving radiologists, through a deep learning method. The proposed approach in this paper used a customized 14 layers ResNet-14 architecture of convolutional neural network (CNN) with six different directions by using class balancing and data augmentation. The performance was evaluated using accuracy, sensitivity, specificity, precision and F1 score of our customized ResNet-14 deep learning architecture with hybrid class balancing and real-time data augmentation after 5-fold cross-validation, with results of 0.920%, 0.916%, 0.946%, 0.916% and 0.923%, respectively. For our proposed ResNet-14 CNN the average area under curves (AUCs) for healthy tear, partial tear and fully ruptured tear had results of 0.980%, 0.970%, and 0.999%, respectively. The proposing diagnostic results indicated that our model could be used to detect automatically and evaluate ACL injuries in athletes using the proposed deep-learning approach.

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Acceleration of Knee MRI Cancellous bone Classification on Google Colaboratory using Convolutional Neural Network

Cited by 28 publications

References 18 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

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

Efficient Detection of Knee Anterior Cruciate Ligament from Magnetic Resonance Imaging Using Deep Learning Approach

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