Improved Deep Convolutional Neural Network to Classify Osteoarthritis from Anterior Cruciate Ligament Tear Using Magnetic Resonance Imaging

Awan, Mazhar Javed; Rahim, Mohd Shafry Mohd; Salim, Naomie; Rehman, Amjad; Nobanee, Haitham; Shabir, Hassan

doi:10.3390/jpm11111163

Cited by 40 publications

(21 citation statements)

References 48 publications

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“…The SPNN [ 12 ] is inspired by the pyramidal neural network (PyraNet) [ 13 ], which in turn was bioinspired by the concepts of receptive fields of human vision. The Pyrenees have two sets of layers: a 2D layer set and a 1D layer set.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Assessing Deep Learning Techniques for the Recognition of Tropical Disease in Images from Parasitological Exams

Hamad

Meraf

2022

Bioinorganic Chemistry and Applications

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Schistosoma mansoni is one of the tropical diseases with the greatest epidemic reach in the world. One of the WHO guidelines is the prior and efficient diagnosis for mapping foci and applying the appropriate treatment of infected people. The current process for diagnosis still depends on an analysis of parasitological exams performed by a human being under a laboratory microscope. The area of pattern recognition in images presents itself as a promising alternative to support and automate image-based exams, and deep learning techniques have been successfully applied for this purpose. In order to automate this process, it is proposed in this work the application of deep learning methods for the detection of schistosomiasis eggs, and a comparison is made between two deep learning techniques, convolutional neural network (CNN) and structured pyramidal neural network (SPNN). The results obtained in a real database indicate that the techniques are effective in the recognition of schistosomiasis eggs, in which both obtained AUC (area under the curve) above 0.90, with the CNN showing superiority in this aspect. . However, the SPNN proved to be faster than the CNN.

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Section: Introductionmentioning

confidence: 99%

[Retracted] Assessing Deep Learning Techniques for the Recognition of Tropical Disease in Images from Parasitological Exams

Hamad

Meraf

2022

Bioinorganic Chemistry and Applications

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“…Both models were tested for classification using ACL images. According to [ 14 ], the modified CNN model has produced superior results than the regular CNN model in terms of learning rate, accuracy, and other factors.…”

Section: Related Workmentioning

confidence: 99%

A Torn ACL Mapping in Knee MRI Images Using Deep Convolution Neural Network with Inception-v3

Sridhar

Amutharaj

Valsalan

et al. 2022

Journal of Healthcare Engineering

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The anterior cruciate ligaments (ACL) are the fundamental structures in preserving the common biomechanics of the knees and most frequently damaged knee ligaments. An ACL injury is a tear or sprain of the ACL, one of the fundamental ligaments in the knee. ACL damage most generally happens during sports, for example, soccer, ball, football, and downhill skiing, which include sudden stops or changes in direction, jumping, and landings. Magnetic resonance imaging (MRI) has a major role in the field of diagnosis these days. Specifically, it is effective for diagnosing the cruciate ligaments and any related meniscal tears. The primary objective of this research is to detect the ACL tear from MRI knee images, which can be useful to determine the knee abnormality. In this research, a Deep Convolution Neural Network (DCNN) based Inception-v3 deep transfer learning (DTL) model was proposed for classifying the ACL tear MRI images. Preprocessing, feature extraction, and classification are the main processes performed in this research. The dataset utilized in this work was collected from the MRNet database. A total of 1,370 knee MRI images are used for evaluation. 70% of data (959 images) are used for training and testing, and 30% of data (411 images) are used in this model for performance analysis. The proposed DCNN with the Inception-v3 DTL model is evaluated and compared with existing deep learning models like VGG16, VGG19, Xception, and Inception ResNet-v28. The performance metrics like accuracy, precision, recall, specificity, and F-measure are evaluated to estimate the performance analysis of the model. The model has obtained 99.04% training accuracy and 95.42% testing accuracy in performance analysis.

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“…Deep learning has tackled the COVID-19 pandemic and is guided by future developments in COVID-19. Recent work inspired by deep learning models such as convolutional neural networks [107][108][109][110][111][112] could be applied to COVID-19 X Rays, CT Scan and MR images.…”

Section: Future Directions and Recommendationsmentioning

confidence: 99%

Big Data COVID-19 Systematic Literature Review: Pandemic Crisis

et al. 2021

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The COVID-19 pandemic has frightened people worldwide, and coronavirus has become the most commonly used phrase in recent years. Therefore, there is a need for a systematic literature review (SLR) related to Big Data applications in the COVID-19 pandemic crisis. The objective is to highlight recent technological advancements. Many studies emphasize the area of the COVID-19 pandemic crisis. Our study categorizes the many applications used to manage and control the pandemic. There is a very limited SLR prospective of COVID-19 with Big Data. Our SLR study picked five databases: Science direct, IEEE Xplore, Springer, ACM, and MDPI. Before the screening, following the recommendation, Preferred Reporting Items for Systematic Reviews and Meta Analyses (PRISMA) were reported for 893 studies from 2019, 2020 and until September 2021. After screening, 60 studies met the inclusion criteria through COVID-19 data statistics, and Big Data analysis was used as the search string. Our research’s findings successfully dealt with COVID-19 healthcare with risk diagnosis, estimation or prevention, decision making, and drug Big Data applications problems. We believe that this review study will motivate the research community to perform expandable and transparent research against the pandemic crisis of COVID-19.

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Improved Deep Convolutional Neural Network to Classify Osteoarthritis from Anterior Cruciate Ligament Tear Using Magnetic Resonance Imaging

Cited by 40 publications

References 48 publications

[Retracted] Assessing Deep Learning Techniques for the Recognition of Tropical Disease in Images from Parasitological Exams

[Retracted] Assessing Deep Learning Techniques for the Recognition of Tropical Disease in Images from Parasitological Exams

A Torn ACL Mapping in Knee MRI Images Using Deep Convolution Neural Network with Inception-v3

Big Data COVID-19 Systematic Literature Review: Pandemic Crisis

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