Performance analysis of pre-trained transfer learning models for the classification of the rolling bearing faults

Sharma, Pavan; Amhia, Hemant; Sharma, Sunil Datt

doi:10.1088/1742-6596/2070/1/012141

Cited by 3 publications

(2 citation statements)

References 13 publications

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“…Pre-trained models(vgg19,vgg16) have been employed for TL in this study, and the final three layers of the model have been fine-tuned to categorize ten different types of defects. When training a network, transfer learning is a simpler method than starting from scratch with a deep learning model [19]. In general, transfer learning is an effective deep learning technique that enables models to use information from datasets or prior tasks to enhance performance on new tasks.…”

Section: B Pre-trained Model and Transfer Learningmentioning

confidence: 99%

Medical image Classification using Transfer Learning: Convolutional Neural Network Models approach

Zobeda Hatif Naji Al-Azzwi

2024

jes

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A relatively low life expectancy in their greatest grade is caused by more debilitating diseases in the medical sector. Any kind of misdiagnosis may lead to improper medical intervention and lower the likelihood that a patient will survive. Making an appropriate treatment plan to cure and enhance the quality of life for patients suffering from any kind of illness starts with an accurate diagnosis. Convolutional neural networks and computer-aided disease detection systems have produced success stories and advanced the science of machine learning significantly. Compared to more conventional neural network layers, the deep convolutional layers automatically extract significant and reliable characteristics from the input space. Within the suggested structure, we use two convolutional neural network architectures (VGG16 and VGG19) to perform two types of medical imaging (x-ray and MRI) investigations in order to categorize brain tumors as normal and up normal as well as classify x-ray pneumonia or normal. The transfer learning strategies by VGG16 and vgg19 models that is, using MRI and x-ray to fine-tune and freeze are then examined in each investigation. In order to increase dataset samples and decrease the likelihood of over-fitting, data augmentation techniques are performed to MRI slices and x-rays for results that can be more broadly utilized. In the proposed studies, the fine-tune VGG19, VGG16 architecture attained highest accuracy up to 0.95with x-ray and 0.80 respectively. The accuracy0.98 used MRI in terms of classification and detection with VGG19.

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Section: B Pre-trained Model and Transfer Learningmentioning

confidence: 99%

Medical image Classification using Transfer Learning: Convolutional Neural Network Models approach

Zobeda Hatif Naji Al-Azzwi

2024

jes

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“…The most popular models were AlexNet, VGG, Xception, Inception, MobileNet, DenseNet, ResNet, GoogleLeNet, and YOLOs. In (Abu et al, 2022;Sharma et al, 2021;Zhao, 2017), all suggested considering fine-tuning several hyperparameters (feature map, filter size, activation function, pool size, optimiser, learning rate, batch size, epoch, dropout rate, loss function, and evaluation metric) of the pre-trained model.…”

Section: Transfer Learningmentioning

confidence: 99%

Ten deep learning techniques to address small data problems with remote sensing

Safonova¹,

Ghazaryan²,

Stiller³

et al. 2023

Preprint

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Researchers and engineers have increasingly used Deep Learning (DL) for a variety of Remote Sensing (RS) tasks. However, data from local observations or via ground truth is often quite limited for training DL models, especially when these models represent key socio-environmental problems, such as the monitoring of extreme, destructive climate events, biodiversity, and sudden changes in ecosystem states. Such cases, also known as small data problems, pose significant methodological challenges. This review summarises these challenges in the RS domain and the possibility of using emerging DL techniques to overcome them. We show that the small data problem is a common challenge across disciplines and scales that results in poor model generalisability and transferability, yet this has not been investigated in a structured way. We first introduce ten emerging DL techniques: transfer learning, self-supervised learning, semi-supervised learning, few-shot learning, zero-shot learning, active learning, weakly supervised learning, multitask learning, process-aware learning, and ensemble learning; we also include a validation technique known as spatial k-fold cross validation. These techniques have shown promising potential in other scientific disciplines, but have been rarely applied in the RS domain. We also provide guidance on which learning technique to use in various cases, which helps to create a more methodologically robust DL application (and a greater number of them) that can be used to tackle socially important problems with limited data.

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