Abnormality Detection with Deep Learning

Ramésh, R.

doi:10.5204/thesis.eprints.118542

Cited by 2 publications

(4 citation statements)

References 40 publications

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“…The precision and reduction of both validation and training on the dataset are shown in the validation and training graph. The figures (9,10,11) showed that there was initially low accuracy for both training and validation. Nonetheless, the accuracy of both validation and training improved with an increase in training iterations.…”

Section: Resultsmentioning

confidence: 99%

“…This is done by employing Class Activation Maps (CAM) for better interpretability and validation. This model is named MuRAD regulates the impressive performance standards, including an F1 score of 0.822 and Cohen's kappa of 0.699, focusing on the potential of automation in advancing diagnostic accuracy and efficiency in in musculoskeletal radiograph analysis.DenseNet models resulted in impressive AUROC and sensitivity values [9].…”

Section: Literature Surveymentioning

confidence: 99%

“…At the final level, a probability prediction can be obtained by applying an activation function, such as the softmax function. During the training phase, weight updates are guided by a loss function, like the cross-entropy loss, which backpropagates the difference between the forecast and the ground truth [10].…”

Section: Modelsmentioning

confidence: 99%

See 2 more Smart Citations

Enhanced Diagnostic Accuracy in Musculoskeletal Radiography: A Comprehensive Ensemble Approach of Deep Learning Models

BS,

Buchupalli,

Gottimukkula

et al. 2024

Preprint

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The musculoskeletal radiograph (MURA) dataset, which is the largest bone x-ray dataset is an important resource for training and testing the machine learning model, as it has been specifically analyzed and well-classified by specially trained radiologists. Four CNN architectures have separately trained and evaluated the dataset ( DenseNet201 GoogLeNet, ResNet50, and Inception-ResNet-V2). The results of the algorithms showed variation in recall and accuracy. In addition, multiple-architecture ensemble models performed better than single models, highlighting the benefits of ensembling techniques to improve prediction accuracy. Combining multiple deep learning models reduces the risk of overfitting, increases predictive stability, improves accuracy, and optimizes the dataset. The integration of several models considers dataset robustness and uncertainty reduction. This ensembling process enables the development of accurate diagnostic tools for upper limb bone-related problems using the MURA dataset and other radiographic datasets. This work shows the use of ensemble methodologies, optimization of architecture, and various techniques, such as using data augmentation and demonstrating the ability of CNN to generalize musculoskeletal diseases. The results of this work can contribute to the creation of more effective diagnostic tools for the healthcare industry.

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Section: Resultsmentioning

confidence: 99%

Section: Literature Surveymentioning

confidence: 99%

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Enhanced Diagnostic Accuracy in Musculoskeletal Radiography: A Comprehensive Ensemble Approach of Deep Learning Models

BS,

Buchupalli,

Gottimukkula

et al. 2024

Preprint

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show abstract

“…A 3D Convolutional Neural Network (C3D) with UCSD dataset has been chosen in [15] for feature extraction and the simulations are performed on MATLAB to achieve patch-based detection and frame-based detection. Moreover, an end-to-end deep model named as Convolutional DLSTM (ConvDLSTM) has been proposed in [16] to understand a crowd scene.…”

Section: Related Workmentioning

confidence: 99%

Identification of Anomalous Behavioral Patterns in Crowd Scenes

Yang¹,

Wang²,

Zhou³

et al. 2022

Computers, Materials &Amp; Continua

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Real time crowd anomaly detection and analyses has become an active and challenging area of research in computer vision since the last decade. The emerging need of crowd management and crowd monitoring for public safety has widen the countless paths of deep learning methodologies and architectures. Although, researchers have developed many sophisticated algorithms but still it is a challenging and tedious task to manage and monitor crowd in real time. The proposed research work focuses on detection of local and global anomaly detection of crowd. Fusion of spatial-temporal features assist in differentiation of feature trained using Mask R-CNN with Resnet101 as a backbone architecture for feature extraction. The data from, BIWI Walking Pedestrian dataset and the Crowds-By-Examples (CBE) dataset and Self-Generated dataset has been used for experimentation. The data deals with different situations like one set of data deals with normal situations like people walking and acting individually, in a group or in a dense crowd. The other set of data contains images four unique anomalies like fight, accident, explosion and people behaving normally. The simulated results show that in terms of precision and recall, our system performs well with Self-Generated dataset. Moreover, our system uses an early stopping mechanism, which allows our system to outperform to make our model efficient. That is why, on 89th epoch our system starts generating finest results.

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Abnormality Detection with Deep Learning

Abstract: Speech, audio, image and video technology (SAIVT)Science

Cited by 2 publications

References 40 publications

Enhanced Diagnostic Accuracy in Musculoskeletal Radiography: A Comprehensive Ensemble Approach of Deep Learning Models

Enhanced Diagnostic Accuracy in Musculoskeletal Radiography: A Comprehensive Ensemble Approach of Deep Learning Models

Identification of Anomalous Behavioral Patterns in Crowd Scenes

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