scholarly journals Deep transfer learning - based automated detection of COVID-19 from lung CT scan slices

2020 ◽  
Author(s):  
Sakshi Ahuja ◽  
Bijaya Ketan Panigrahi ◽  
Nilanjan Dey ◽  
Venkatesan Rajinikanth ◽  
Tapan Kumar Gandhi
Keyword(s):  
Ct Scan ◽  
Transfer Learning ◽  
Data Augmentation ◽  
Research Work ◽  
Detection Accuracy ◽  
Phase Detection ◽  
Detection Model ◽  
Three Phase ◽  
The Common ◽  
Lung Ct

In the proposed research work; the COVID-19 is detected using transfer learning from CT scan images decomposed to three-level using stationary wavelet. A three-phase detection model is proposed to improve the detection accuracy and the procedures are as follows; Phase1- data augmentation using stationary wavelets, Phase2- COVID-19 detection using pre-trained CNN model and Phase3- abnormality localization in CT scan images. This work has considered the well known pre-trained architectures, such as ResNet18, ResNet50, ResNet101, and SqueezeNet for the experimental evaluation. In this work, 70% of images are considered to train the network and 30% images are considered to validate the network. The performance of the considered architectures is evaluated by computing the common performance measures.<br><br>

scholarly journals Deep transfer learning - based automated detection of COVID-19 from lung CT scan slices

2020 ◽  
Author(s):  
Sakshi Ahuja ◽  
Bijaya Ketan Panigrahi ◽  
Nilanjan Dey ◽  
Venkatesan Rajinikanth ◽  
Tapan Kumar Gandhi
Keyword(s):  
Ct Scan ◽  
Transfer Learning ◽  
Data Augmentation ◽  
Research Work ◽  
Detection Accuracy ◽  
Phase Detection ◽  
Detection Model ◽  
Three Phase ◽  
The Common ◽  
Lung Ct

In the proposed research work; the COVID-19 is detected using transfer learning from CT scan images decomposed to three-level using stationary wavelet. A three-phase detection model is proposed to improve the detection accuracy and the procedures are as follows; Phase1- data augmentation using stationary wavelets, Phase2- COVID-19 detection using pre-trained CNN model and Phase3- abnormality localization in CT scan images. This work has considered the well known pre-trained architectures, such as ResNet18, ResNet50, ResNet101, and SqueezeNet for the experimental evaluation. In this work, 70% of images are considered to train the network and 30% images are considered to validate the network. The performance of the considered architectures is evaluated by computing the common performance measures.<br><br>

scholarly journals Deep transfer learning - based automated detection of COVID-19 from lung CT scan slices

2020 ◽  
Author(s):  
Sakshi Ahuja ◽  
Bijaya Ketan Panigrahi ◽  
Nilanjan Dey ◽  
Tapan Gandhi ◽  
Venkatesan Rajinikanth
Keyword(s):  
Ct Scan ◽  
Transfer Learning ◽  
Data Augmentation ◽  
Research Work ◽  
Detection Accuracy ◽  
Phase Detection ◽  
Detection Model ◽  
Three Phase ◽  
The Common ◽  
Lung Ct

In the proposed research work; the COVID-19 is detected using transfer learning from CT scan images decomposed to three-level using stationary wavelet. A three-phase detection model is proposed to improve the detection accuracy and the procedures are as follows; Phase1- data augmentation using stationary wavelets, Phase2- COVID-19 detection using pre-trained CNN model and Phase3- abnormality localization in CT scan images. This work has considered the well known pre-trained architectures, such as ResNet18, ResNet50, ResNet101, and SqueezeNet for the experimental evaluation. In this work, 70% of images are considered to train the network and 30% images are considered to validate the network. The performance of the considered architectures is evaluated by computing the common performance measures.<br><br>

scholarly journals Robust Detection of Bearing Early Fault Based on Deep Transfer Learning

Electronics ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 323 ◽  
Author(s):  
Wentao Mao ◽  
Di Zhang ◽  
Siyu Tian ◽  
Jiamei Tang
Keyword(s):  
Transfer Learning ◽  
Normal State ◽  
Low Rank ◽  
Detection Methods ◽  
Support Vector ◽  
Detection Accuracy ◽  
Robust Detection ◽  
Detection Model ◽  
Performance Reduction ◽  
The Common

In recent years, machine learning techniques have been proven to be a promising tool for early fault detection of rolling bearings. In many actual applications, however, bearing whole-life data are not easy to be historically accumulated, while insufficient data may result in training a detection model that is not good enough. If utilizing the available data under different working conditions to facilitate model training, the data distribution of different bearings are usually quite different, which does not meet the precondition of i n d e p e n d e n t a n d i d e n t i c a l d i s t r i b u t i o n ( i . i . d ) and tends to cause performance reduction. In addition, disturbed by the unstable noise under complex conditions, most of the current detection methods are inclined to raise false alarms, so that the reliability of detection results needs to be improved. To solve these problems, a robust detection method for bearings early fault is proposed based on deep transfer learning. The method includes offline stage and online stage. In the offline stage, by introducing a deep auto-encoder network with domain adaptation, the distribution inconsistency of normal state data among different bearings can be weakened, then the common feature representation of the normal state is obtained. With the extracted common features, a new state assessment method based on the robust deep auto-encoder network is proposed to evaluate the boundary between normal state and early fault state in the low-rank feature space. By training a support vector machine classifier, the detection model is established. In the online stage, along with the data batch arriving sequentially, the features of target bearing are extracted using the common representation learnt in the offline stage, and online detection is conducted by feeding them into the SVM model. Experimental results on IEEE PHM Challenge 2012 bearing dataset and XJTU-SY dataset show that the proposed approach outperforms several state-of-the-art detection methods in terms of detection accuracy and false alarm rate.

scholarly journals Augmenting Crop Detection for Precision Agriculture with Deep Visual Transfer Learning—A Case Study of Bale Detection

2020 ◽  
Vol 13 (1) ◽  
pp. 23
Author(s):  
Wei Zhao ◽  
William Yamada ◽  
Tianxin Li ◽  
Matthew Digman ◽  
Troy Runge
Keyword(s):  
Object Detection ◽  
Transfer Learning ◽  
Precision Agriculture ◽  
Crop Production ◽  
Domain Adaptation ◽  
Training Data ◽  
Detection Accuracy ◽  
Detection Model ◽  
Agriculture Products

In recent years, precision agriculture has been researched to increase crop production with less inputs, as a promising means to meet the growing demand of agriculture products. Computer vision-based crop detection with unmanned aerial vehicle (UAV)-acquired images is a critical tool for precision agriculture. However, object detection using deep learning algorithms rely on a significant amount of manually prelabeled training datasets as ground truths. Field object detection, such as bales, is especially difficult because of (1) long-period image acquisitions under different illumination conditions and seasons; (2) limited existing prelabeled data; and (3) few pretrained models and research as references. This work increases the bale detection accuracy based on limited data collection and labeling, by building an innovative algorithms pipeline. First, an object detection model is trained using 243 images captured with good illimitation conditions in fall from the crop lands. In addition, domain adaptation (DA), a kind of transfer learning, is applied for synthesizing the training data under diverse environmental conditions with automatic labels. Finally, the object detection model is optimized with the synthesized datasets. The case study shows the proposed method improves the bale detecting performance, including the recall, mean average precision (mAP), and F measure (F1 score), from averages of 0.59, 0.7, and 0.7 (the object detection) to averages of 0.93, 0.94, and 0.89 (the object detection + DA), respectively. This approach could be easily scaled to many other crop field objects and will significantly contribute to precision agriculture.

scholarly journals An Enhanced Hidden Semi-Markov model for Outlier Detection in Multivariate Datasets

2021 ◽  
Author(s):  
G Manoharan ◽  
K Sivakumar
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Outlier Detection ◽  
Hidden Markov ◽  
Research Work ◽  
Detection Accuracy ◽  
Detection Techniques ◽  
Detection Model ◽  
Huge Data ◽  
Proposed Model

Outlier detection in data mining is an important arena where detection models are developed to discover the objects that do not confirm the expected behavior. The generation of huge data in real time applications makes the outlier detection process into more crucial and challenging. Traditional detection techniques based on mean and covariance are not suitable to handle large amount of data and the results are affected by outliers. So it is essential to develop an efficient outlier detection model to detect outliers in the large dataset. The objective of this research work is to develop an efficient outlier detection model for multivariate data employing the enhanced Hidden Semi-Markov Model (HSMM). It is an extension of conventional Hidden Markov Model (HMM) where the proposed model allows arbitrary time distribution in its states to detect outliers. Experimental results demonstrate the better performance of proposed model in terms of detection accuracy, detection rate. Compared to conventional Hidden Markov Model based outlier detection the detection accuracy of proposed model is obtained as 98.62% which is significantly better for large multivariate datasets.

scholarly journals Deep transfer learning-based automated detection of COVID-19 from lung CT scan slices

2020 ◽  
Vol 51 (1) ◽  
pp. 571-585 ◽  
Author(s):  
Sakshi Ahuja ◽  
Bijaya Ketan Panigrahi ◽  
Nilanjan Dey ◽  
Venkatesan Rajinikanth ◽  
Tapan Kumar Gandhi
Keyword(s):  
Ct Scan ◽  
Transfer Learning ◽  
Automated Detection ◽  
Lung Ct ◽  
Lung Ct Scan

scholarly journals Transfer learning-based approach for detecting COVID-19 ailment in lung CT scan

2021 ◽  
pp. 104575
Author(s):  
Vinay Arora ◽  
Eddie Yin-Kwee Ng ◽  
Rohan Singh Leekha ◽  
Medhavi Darshan ◽  
Arshdeep Singh
Keyword(s):  
Ct Scan ◽  
Transfer Learning ◽  
Lung Ct ◽  
Lung Ct Scan

scholarly journals A Postearthquake Multiple Scene Recognition Model Based on Classical SSD Method and Transfer Learning

2020 ◽  
Vol 9 (4) ◽  
pp. 238 ◽  
Author(s):  
Zhiqiang Xu ◽  
Yumin Chen ◽  
Fan Yang ◽  
Tianyou Chu ◽  
Hongyan Zhou
Keyword(s):  
Transfer Learning ◽  
Data Augmentation ◽  
Recognition Performance ◽  
Scene Recognition ◽  
Support Vector ◽  
Detection Accuracy ◽  
Single Shot ◽  
Visual Interpretation ◽  
Model Based ◽  
Original Dataset

The recognition of postearthquake scenes plays an important role in postearthquake rescue and reconstruction. To overcome the over-reliance on expert visual interpretation and the poor recognition performance of traditional machine learning in postearthquake scene recognition, this paper proposes a postearthquake multiple scene recognition (PEMSR) model based on the classical deep learning Single Shot MultiBox Detector (SSD) method. In this paper, a labeled postearthquake scenes dataset is constructed by segmenting acquired remote sensing images, which are classified into six categories: landslide, houses, ruins, trees, clogged and ponding. Due to the insufficiency and imbalance of the original dataset, transfer learning and a data augmentation and balancing strategy are utilized in the PEMSR model. To evaluate the PEMSR model, the evaluation metrics of precision, recall and F1 score are used in the experiment. Multiple experimental test results demonstrate that the PEMSR model shows a stronger performance in postearthquake scene recognition. The PEMSR model improves the detection accuracy of each scene compared with SSD by transfer learning and data augmentation strategy. In addition, the average detection time of the PEMSR model only needs 0.4565s, which is far less than the 8.3472s of the traditional Histogram of Oriented Gradient + Support Vector Machine (HOG+SVM) method.

scholarly journals Exploiting Multiple Optimizers with Transfer Learning Techniques for the Identification of COVID-19 Patients

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Zeming Fan ◽  
Mudasir Jamil ◽  
Muhammad Tariq Sadiq ◽  
Xiwei Huang ◽  
Xiaojun Yu
Keyword(s):  
Early Detection ◽  
Transfer Learning ◽  
Data Augmentation ◽  
Data Transfer ◽  
Training Data ◽  
Detection Accuracy ◽  
Boundary Segment ◽  
Learning Rates ◽  
Learning Techniques ◽  
Average Accuracy

Due to the rapid spread of COVID-19 and its induced death worldwide, it is imperative to develop a reliable tool for the early detection of this disease. Chest X-ray is currently accepted to be one of the reliable means for such a detection purpose. However, most of the available methods utilize large training data, and there is a need for improvement in the detection accuracy due to the limited boundary segment of the acquired images for symptom identifications. In this study, a robust and efficient method based on transfer learning techniques is proposed to identify normal and COVID-19 patients by employing small training data. Transfer learning builds accurate models in a timesaving way. First, data augmentation was performed to help the network for memorization of image details. Next, five state-of-the-art transfer learning models, AlexNet, MobileNetv2, ShuffleNet, SqueezeNet, and Xception, with three optimizers, Adam, SGDM, and RMSProp, were implemented at various learning rates, 1e-4, 2e-4, 3e-4, and 4e-4, to reduce the probability of overfitting. All the experiments were performed on publicly available datasets with several analytical measurements attained after execution with a 10-fold cross-validation method. The results suggest that MobileNetv2 with Adam optimizer at a learning rate of 3e-4 provides an average accuracy, recall, precision, and F-score of 97%, 96.5%, 97.5%, and 97%, respectively, which are higher than those of all other combinations. The proposed method is competitive with the available literature, demonstrating that it could be used for the early detection of COVID-19 patients.

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