A Selective Mitigation Technique of Soft Errors for DNN Models Used in Healthcare Applications: DenseNet201 Case Study

Adam, Khalid; Mohamed, Izzeldin Ibrahim; Ibrahim, Younis

doi:10.1109/access.2021.3076716

Cited by 20 publications

(8 citation statements)

References 49 publications

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“…However, DL accelerators such as GPUs can generate false hardware error that can propagate to the software calculations and effect the accuracy of the model [68].…”

Section: Detection Resultsmentioning

confidence: 99%

Detection and Monitoring of Power Line Corridor From Satellite Imagery Using RetinaNet and K-Mean Clustering

2021

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Monitoring of electrical transmission towers (TTs) is required to maintain the integrity of power lines. One major challenge is monitoring vegetation encroachment that can cause power interruption. Most of the current monitoring techniques use unmanned aerial vehicles (UAV) and airborne photography as an observation medium. However, these methods are expensive and not practical for monitoring wide areas. In this paper, we introduced a new method for monitoring the power line corridor from satellite imagery. The proposed method consists of two stages. In the first stage, we used the existing state-of-the-art RetinaNet deep learning (DL) model to detect the locations of the TTs from satellite imagery. A routing algorithm has been developed to create a path between every adjacent detected TT. In addition to the routing algorithm, a corridor identification algorithm has been established for extracting the power line corridor area. In the second stage, the k-mean clustering algorithm has been used to highlight the VE regions within the power line corridor area after converting the target satellite image into hue, saturation, and value (HSV) color space. The proposed monitoring system was able to detect TTs from satellite imagery with a mean average precision (mAP) of 72.45% for an Intersection of Union (IoU) threshold of 0.5 and 85.21% for IoU threshold of 0.3. Also, the monitoring system was able to successfully discriminate high-and low-density vegetation regions within the power line corridor area.INDEX TERMS Deep learning, K-mean, satellite images, transmission tower detection.

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“…However, DL accelerators such as GPUs can generate false hardware error that can propagate to the software calculations and effect the accuracy of the model [68].…”

Section: Detection Resultsmentioning

confidence: 99%

Detection and Monitoring of Power Line Corridor From Satellite Imagery Using RetinaNet and K-Mean Clustering

2021

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“…This model architecture consists of 201 convolutional layers, 98 route layers, one average pooling layer, four max-pool layers, and Softmax [ 40 ]. It contains a total of 6,992,806 trainable parameters [ 41 ].…”

Section: Methodsmentioning

confidence: 99%

A Robust Framework Combining Image Processing and Deep Learning Hybrid Model to Classify Cardiovascular Diseases Using a Limited Number of Paper-Based Complex ECG Images

et al. 2022

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Heart disease can be life-threatening if not detected and treated at an early stage. The electrocardiogram (ECG) plays a vital role in classifying cardiovascular diseases, and often physicians and medical researchers examine paper-based ECG images for cardiac diagnosis. An automated heart disease prediction system might help to classify heart diseases accurately at an early stage. This study aims to classify cardiac diseases into five classes with paper-based ECG images using a deep learning approach with the highest possible accuracy and the lowest possible time complexity. This research consists of two approaches. In the first approach, five deep learning models, InceptionV3, ResNet50, MobileNetV2, VGG19, and DenseNet201, are employed. In the second approach, an integrated deep learning model (InRes-106) is introduced, combining InceptionV3 and ResNet50. This model is developed as a deep convolutional neural network capable of extracting hidden and high-level features from images. An ablation study is conducted on the proposed model altering several components and hyperparameters, improving the performance even further. Before training the model, several image pre-processing techniques are employed to remove artifacts and enhance the image quality. Our proposed hybrid InRes-106 model performed best with a testing accuracy of 98.34%. The InceptionV3 model acquired a testing accuracy of 90.56%, the ResNet50 89.63%, the DenseNet201 88.94%, the VGG19 87.87%, and the MobileNetV2 achieved 80.56% testing accuracy. The model is trained with a k-fold cross-validation technique with different k values to evaluate the robustness further. Although the dataset contains a limited number of complex ECG images, our proposed approach, based on various image pre-processing techniques, model fine-tuning, and ablation studies, can effectively diagnose cardiac diseases.

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“…However, this is a different form of comparative analysis, where the outcome of proposed system (performance parameters with segmentation) is further subjected to 7 version of neural network viz. Resnet 18 [32], Resnet 50 [33], Resnet101 [34], VGG19 [35], Densenet201 [36], Squeezenet [37], and Mobilenet [38].…”

Section: Extensive Analysis With Different Versions Of Cnnmentioning

confidence: 99%

An Add-on CNN based Model for the Detection of Tuberculosis using Chest X-ray Images

Roopa¹,

Mamatha²

2023

IJACSA

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MachineLearning has been potentially contributing towards smart diagnosis in the medical domain for more than a decade with a target towards achieving higher accuracy in detection and classification. However, from the perspective of medical image processing, the contribution of machine learning towards segmentation is not been much to find in recent times. The proposed study considers a use case of Tuberculosis detection and classification from chest x-rays where a unique machine learning approach of Convolution Neural Network is adopted for segmentation of lung images from CXR. A computational framework is developed that performs segmentation, feature extraction, detection, and classification. The proposed system's study outcome is analyzed with and without segmentation over existing machine learning models to exhibit 99.85% accuracy, which is the highest score to date in contrast to existing approaches found in the literature. The study outcome based on the comparative analysis exhibits the effectiveness of the proposed system.

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A Selective Mitigation Technique of Soft Errors for DNN Models Used in Healthcare Applications: DenseNet201 Case Study

Cited by 20 publications

References 49 publications

Detection and Monitoring of Power Line Corridor From Satellite Imagery Using RetinaNet and K-Mean Clustering

Detection and Monitoring of Power Line Corridor From Satellite Imagery Using RetinaNet and K-Mean Clustering

A Robust Framework Combining Image Processing and Deep Learning Hybrid Model to Classify Cardiovascular Diseases Using a Limited Number of Paper-Based Complex ECG Images

An Add-on CNN based Model for the Detection of Tuberculosis using Chest X-ray Images

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