A Transfer Learning Approach for Lumbar Spine Disc State Classification

Al-Kubaisi, Ali; Khamiss, Nasser N.

doi:10.3390/electronics11010085

Cited by 9 publications

(5 citation statements)

References 41 publications

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“…In this learning type, a problem is learned by a model to be applied as a reference for other tasks [123][124][125]. This method is viable if the process is close to the primary problem and the related task demands plenty of data [23,126].…”

Section: Transfer Learningmentioning

confidence: 99%

“…It will offer the chance to use a shallow model with the desired input size. By using the same approach, several published articles have improved the effectiveness of these solutions for medical images and other domains [22,123,164,[261][262][263][264][265].…”

Section: • Research Problem In Transfer Learning For Medical Imagingmentioning

confidence: 99%

“…Industrial automation [128,234,[279][280][281][282][283][284][285] Medical imaging [22,23,123,124,213,256,[286][287][288][289][290] Wireless communications [291][292][293][294][295][296][297] Plant diseases [262,[298][299][300][301][302][303] Machinery fault [304][305][306][307][308][309][310] Software defect [311][312][313][314][315] Activity recognition [316][317][318][319]<...…”

Section: Field Literaturementioning

confidence: 99%

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A survey on deep learning tools dealing with data scarcity: definitions, challenges, solutions, tips, and applications

et al. 2023

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Data scarcity is a major challenge when training deep learning (DL) models. DL demands a large amount of data to achieve exceptional performance. Unfortunately, many applications have small or inadequate data to train DL frameworks. Usually, manual labeling is needed to provide labeled data, which typically involves human annotators with a vast background of knowledge. This annotation process is costly, time-consuming, and error-prone. Usually, every DL framework is fed by a significant amount of labeled data to automatically learn representations. Ultimately, a larger amount of data would generate a better DL model and its performance is also application dependent. This issue is the main barrier for many applications dismissing the use of DL. Having sufficient data is the first step toward any successful and trustworthy DL application. This paper presents a holistic survey on state-of-the-art techniques to deal with training DL models to overcome three challenges including small, imbalanced datasets, and lack of generalization. This survey starts by listing the learning techniques. Next, the types of DL architectures are introduced. After that, state-of-the-art solutions to address the issue of lack of training data are listed, such as Transfer Learning (TL), Self-Supervised Learning (SSL), Generative Adversarial Networks (GANs), Model Architecture (MA), Physics-Informed Neural Network (PINN), and Deep Synthetic Minority Oversampling Technique (DeepSMOTE). Then, these solutions were followed by some related tips about data acquisition needed prior to training purposes, as well as recommendations for ensuring the trustworthiness of the training dataset. The survey ends with a list of applications that suffer from data scarcity, several alternatives are proposed in order to generate more data in each application including Electromagnetic Imaging (EMI), Civil Structural Health Monitoring, Medical imaging, Meteorology, Wireless Communications, Fluid Mechanics, Microelectromechanical system, and Cybersecurity. To the best of the authors’ knowledge, this is the first review that offers a comprehensive overview on strategies to tackle data scarcity in DL.

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Section: Transfer Learningmentioning

confidence: 99%

Section: • Research Problem In Transfer Learning For Medical Imagingmentioning

confidence: 99%

Section: Field Literaturementioning

confidence: 99%

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A survey on deep learning tools dealing with data scarcity: definitions, challenges, solutions, tips, and applications

et al. 2023

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“…Performance measures play an essential role in testing and evaluation in developing any machine learning model [82]. Furthermore, evaluation metrics are essential for measuring the accuracy of any classifier, as the results of any classifier may be good against specific metrics and may not be good or bad against other metrics [83]. Therefore, metrics should be used in the training and testing phases to evaluate the system.…”

Section: Evaluation Metricsmentioning

confidence: 99%

PRCnet: An Efficient Model for Automatic Detection of Brain Tumor in MRI Images

Farhan,

Khalid,

Manzoor

2023

Preprint

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Brain tumors are the most prevalent and life-threatening cancer; an early and accurate diagnosis of brain tumors increases the chances of patient survival and treatment planning. However, manual tumor detection is a complex, cumbersome and time-consuming task and is prone to errors, which relies on the radiologist’s experience. As a result, the development of accurate and automatic system for tumor detection is critical. In this paper, we proposed a new model called Parallel Residual Convolutional Network (PRCnet) model to classify brain tumors from Magnetic Resonance Imaging. The PCRnet model uses several techniques (such as filters of different sizes with parallel layers, connections between layers, batch normalization layer, and ReLU) and dropout layer to overcome the over-fitting problem, for achieving accurate and automatic classification of brain tumors. The PRCnet model is trained and tested on two different datasets and obtained an accuracy of 94.77% and 97.1% for dataset A and dataset B, respectively which is way better as compared to the state-of-the-art models.

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“…In transfer learning, as shown in Figure 6 [ 31 , 33 ], learning parameters are transferred by the model trained on large data, for a long time on GPUs or TPUs, to our desired model and then freeze the parameters while training our model. Fine-tuning the transfer learning model also helps to achieve the desired result for the particular task.…”

Section: Proposed Workmentioning

confidence: 99%

Fault Detection in Induction Motor Using Time Domain and Spectral Imaging-Based Transfer Learning Approach on Vibration Data

Misra

Kumar

Sayyad

et al. 2022

Sensors

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The induction motor plays a vital role in industrial drive systems due to its robustness and easy maintenance but at the same time, it suffers electrical faults, mainly rotor faults such as broken rotor bars. Early shortcoming identification is needed to lessen support expenses and hinder high costs by using failure detection frameworks that give features extraction and pattern grouping of the issue to distinguish the failure in an induction motor using classification models. In this paper, the open-source dataset of the rotor with the broken bars in a three-phase induction motor available on the IEEE data port is used for fault classification. The study aims at fault identification under various loading conditions on the rotor of an induction motor by performing time, frequency, and time-frequency domain feature extraction. The extracted features are provided to the models to classify between the healthy and faulty rotors. The extracted features from the time and frequency domain give an accuracy of up to 87.52% and 88.58%, respectively, using the Random-Forest (RF) model. Whereas, in time-frequency, the Short Time Fourier Transform (STFT) based spectrograms provide reasonably high accuracy, around 97.67%, using a Convolutional Neural Network (CNN) based fine-tuned transfer learning framework for diagnosing induction motor rotor bar severity under various loading conditions.

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A Transfer Learning Approach for Lumbar Spine Disc State Classification

Cited by 9 publications

References 41 publications

A survey on deep learning tools dealing with data scarcity: definitions, challenges, solutions, tips, and applications

A survey on deep learning tools dealing with data scarcity: definitions, challenges, solutions, tips, and applications

PRCnet: An Efficient Model for Automatic Detection of Brain Tumor in MRI Images

Fault Detection in Induction Motor Using Time Domain and Spectral Imaging-Based Transfer Learning Approach on Vibration Data

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