A Transfer Learning and Performance Comparison of Deep Learning Models for Pedestrian Classification under Automobile Driving Environment

The COVID-19 pandemic requires the rapid isolation of infected patients. Thus, high-sensitivity radiology images could be a key technique to diagnose patients besides the polymerase chain reaction approach. Deep learning algorithms are proposed in several studies to detect COVID-19 symptoms due to the success in chest radiography image classification, cost efficiency, lack of expert radiologists, and the need for faster processing in the pandemic area. Most of the promising algorithms proposed in different studies are based on pre-trained deep learning models. Such open-source models and lack of variation in the radiology image-capturing environment make the diagnosis system vulnerable to adversarial attacks such as fast gradient sign method (FGSM) attack. This study therefore explored the potential vulnerability of pre-trained convolutional neural network algorithms to the FGSM attack in terms of two frequently used models, VGG16 and Inception-v3. Firstly, we developed two transfer learning models for X-ray and CT image-based COVID-19 classification and analyzed the performance extensively in terms of accuracy, precision, recall, and AUC. Secondly, our study illustrates that misclassification can occur with a very minor perturbation magnitude, such as 0.009 and 0.003 for the FGSM attack in these models for X-ray and CT images, respectively, without any effect on the visual perceptibility of the perturbation. In addition, we demonstrated that successful FGSM attack can decrease the classification performance to 16.67% and 55.56% for X-ray images, as well as 36% and 40% in the case of CT images for VGG16 and Inception-v3, respectively, without any human-recognizable perturbation effects in the adversarial images. Finally, we analyzed that correct class probability of any test image which is supposed to be 1, can drop for both considered models and with increased perturbation; it can drop to 0.24 and 0.17 for the VGG16 model in cases of X-ray and CT images, respectively. Thus, despite the need for data sharing and automated diagnosis, practical deployment of such program requires more robustness.

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Study on Human Activity Recognition Using Semi-Supervised Active Transfer Learning

Sensors ◽

10.3390/s21082760 ◽

2021 ◽

Vol 21 (8) ◽

pp. 2760

Author(s):

Seungmin Oh ◽

Akm Ashiquzzaman ◽

Dongsu Lee ◽

Yeonggwang Kim ◽

Jinsul Kim

Keyword(s):

Deep Learning ◽

Transfer Learning ◽

Activity Recognition ◽

Human Activity ◽

Random Sampling ◽

Data Gathering ◽

Human Activity Recognition ◽

Learning Models ◽

Active Transfer ◽

Existing Data

In recent years, various studies have begun to use deep learning models to conduct research in the field of human activity recognition (HAR). However, there has been a severe lag in the absolute development of such models since training deep learning models require a lot of labeled data. In fields such as HAR, it is difficult to collect data and there are high costs and efforts involved in manual labeling. The existing methods rely heavily on manual data collection and proper labeling of the data, which is done by human administrators. This often results in the data gathering process often being slow and prone to human-biased labeling. To address these problems, we proposed a new solution for the existing data gathering methods by reducing the labeling tasks conducted on new data based by using the data learned through the semi-supervised active transfer learning method. This method achieved 95.9% performance while also reducing labeling compared to the random sampling or active transfer learning methods.

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Transfer Learning of The ResNet-18 and DenseNet-121 Model Used to Diagnose Intracranial Hemorrhage in CT Scanning

Current Pharmaceutical Design ◽

10.2174/1381612827666211213143357 ◽

2021 ◽

Vol 27 ◽

Author(s):

Qi Zhou ◽

Wenjie Zhu ◽

Fuchen Li ◽

Mingqing Yuan ◽

Linfeng Zheng ◽

...

Keyword(s):

Deep Learning ◽

Transfer Learning ◽

Intracranial Hemorrhage ◽

Intraventricular Hemorrhage ◽

Learning Model ◽

Normal Group ◽

Subdural Hemorrhage ◽

Model Assessment ◽

Learning Models ◽

Deep Learning Model

Objective: To verify the ability of the deep learning model in identifying five subtypes and normal images in noncontrast enhancement CT of intracranial hemorrhage. Method: A total of 351 patients (39 patients in the normal group, 312 patients in the intracranial hemorrhage group) performed with intracranial hemorrhage noncontrast enhanced CT were selected, with 2768 images in total (514 images for the normal group, 398 images for the epidural hemorrhage group, 501 images for the subdural hemorrhage group, 497 images for the intraventricular hemorrhage group, 415 images for the cerebral parenchymal hemorrhage group, and 443 images for the subarachnoid hemorrhage group). Based on the diagnostic reports of two radiologists with more than 10 years of experience, the ResNet-18 and DenseNet-121 deep learning models were selected. Transfer learning was used. 80% of the data was used for training models, 10% was used for validating model performance against overfitting, and the last 10% was used for the final evaluation of the model. Assessment indicators included accuracy, sensitivity, specificity, and AUC values. Results: The overall accuracy of ResNet-18 and DenseNet-121 models were 89.64% and 82.5%, respectively. The sensitivity and specificity of identifying five subtypes and normal images were above 0.80. The sensitivity of DenseNet-121 model to recognize intraventricular hemorrhage and cerebral parenchymal hemorrhage was lower than 0.80, 0.73, and 0.76 respectively. The AUC values of the two deep learning models were above 0.9. Conclusion: The deep learning model can accurately identify the five subtypes of intracranial hemorrhage and normal images, and it can be used as a new tool for clinical diagnosis in the future.

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Comparative study on different Deep Learning models for Skin Lesion Classification using transfer learning approach

International Journal of Scientific and Research Publications (IJSRP) ◽

10.29322/ijsrp.11.01.2021.p10923 ◽

2020 ◽

Vol 11 (1) ◽

pp. 219-229

Author(s):

Saswat Panda ◽

Abhishek Sunil Tiwari ◽

Manas Ranjan Prusty

Keyword(s):

Deep Learning ◽

Skin Lesion ◽

Comparative Study ◽

Transfer Learning ◽

Learning Approach ◽

Learning Models ◽

Lesion Classification

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Leveraging Natural Language Processing Applications Using Machine Learning

Handbook of Research on Emerging Trends and Applications of Machine Learning - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-5225-9643-1.ch016 ◽

2020 ◽

pp. 338-360

Author(s):

Janjanam Prabhudas ◽

C. H. Pradeep Reddy

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Natural Language Processing ◽

Natural Language ◽

Language Processing ◽

Text Summarization ◽

Feature Representation ◽

Learning Models ◽

Primary Focus ◽

And Performance

The enormous increase of information along with the computational abilities of machines created innovative applications in natural language processing by invoking machine learning models. This chapter will project the trends of natural language processing by employing machine learning and its models in the context of text summarization. This chapter is organized to make the researcher understand technical perspectives regarding feature representation and their models to consider before applying on language-oriented tasks. Further, the present chapter revises the details of primary models of deep learning, its applications, and performance in the context of language processing. The primary focus of this chapter is to illustrate the technical research findings and gaps of text summarization based on deep learning along with state-of-the-art deep learning models for TS.

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Optimizing the Performance of Breast Cancer Classification by Employing the Same Domain Transfer Learning from Hybrid Deep Convolutional Neural Network Model

Electronics ◽

10.3390/electronics9030445 ◽

2020 ◽

Vol 9 (3) ◽

pp. 445 ◽

Cited By ~ 9

Author(s):

Laith Alzubaidi ◽

Omran Al-Shamma ◽

Mohammed A. Fadhel ◽

Laith Farhan ◽

Jinglan Zhang ◽

...

Keyword(s):

Breast Cancer ◽

Deep Learning ◽

Transfer Learning ◽

Cancer Diagnosis ◽

Classification Accuracy ◽

Cancer Classification ◽

Learning Models ◽

Proposed Model ◽

Domain Transfer ◽

Domain Transfer Learning

Breast cancer is a significant factor in female mortality. An early cancer diagnosis leads to a reduction in the breast cancer death rate. With the help of a computer-aided diagnosis system, the efficiency increased, and the cost was reduced for the cancer diagnosis. Traditional breast cancer classification techniques are based on handcrafted features techniques, and their performance relies upon the chosen features. They also are very sensitive to different sizes and complex shapes. However, histopathological breast cancer images are very complex in shape. Currently, deep learning models have become an alternative solution for diagnosis, and have overcome the drawbacks of classical classification techniques. Although deep learning has performed well in various tasks of computer vision and pattern recognition, it still has some challenges. One of the main challenges is the lack of training data. To address this challenge and optimize the performance, we have utilized a transfer learning technique which is where the deep learning models train on a task, and then fine-tune the models for another task. We have employed transfer learning in two ways: Training our proposed model first on the same domain dataset, then on the target dataset, and training our model on a different domain dataset, then on the target dataset. We have empirically proven that the same domain transfer learning optimized the performance. Our hybrid model of parallel convolutional layers and residual links is utilized to classify hematoxylin–eosin-stained breast biopsy images into four classes: invasive carcinoma, in-situ carcinoma, benign tumor and normal tissue. To reduce the effect of overfitting, we have augmented the images with different image processing techniques. The proposed model achieved state-of-the-art performance, and it outperformed the latest methods by achieving a patch-wise classification accuracy of 90.5%, and an image-wise classification accuracy of 97.4% on the validation set. Moreover, we have achieved an image-wise classification accuracy of 96.1% on the test set of the microscopy ICIAR-2018 dataset.

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Deep Transfer Learning for Source Code Modeling

International Journal of Software Engineering and Knowledge Engineering ◽

10.1142/s0218194020500230 ◽

2020 ◽

Vol 30 (05) ◽

pp. 649-668

Author(s):

Yasir Hussain ◽

Zhiqiu Huang ◽

Yu Zhou ◽

Senzhang Wang

Keyword(s):

Neural Network ◽

Deep Learning ◽

Transfer Learning ◽

Machine Learning Models for Abnormality Detection in Musculoskeletal Radiographs

Reports ◽

10.3390/reports2040026 ◽

2019 ◽

Vol 2 (4) ◽

pp. 26 ◽

Cited By ~ 1

Author(s):

Govind Chada

Keyword(s):

Machine Learning ◽

Primary Care ◽

Deep Learning ◽

Transfer Learning ◽

Primary Care Physicians ◽

Screening Tools ◽

Learning Approaches ◽

Limited Data ◽

Learning Models ◽

High Recognition Accuracy

Increasing radiologist workloads and increasing primary care radiology services make it relevant to explore the use of artificial intelligence (AI) and particularly deep learning to provide diagnostic assistance to radiologists and primary care physicians in improving the quality of patient care. This study investigates new model architectures and deep transfer learning to improve the performance in detecting abnormalities of upper extremities while training with limited data. DenseNet-169, DenseNet-201, and InceptionResNetV2 deep learning models were implemented and evaluated on the humerus and finger radiographs from MURA, a large public dataset of musculoskeletal radiographs. These architectures were selected because of their high recognition accuracy in a benchmark study. The DenseNet-201 and InceptionResNetV2 models, employing deep transfer learning to optimize training on limited data, detected abnormalities in the humerus radiographs with 95% CI accuracies of 83–92% and high sensitivities greater than 0.9, allowing for these models to serve as useful initial screening tools to prioritize studies for expedited review. The performance in the case of finger radiographs was not as promising, possibly due to the limitations of large inter-radiologist variation. It is suggested that the causes of this variation be further explored using machine learning approaches, which may lead to appropriate remediation.

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