A weighted multi-feature transfer learning framework for intelligent medical decision making

Ye, Yun; Guo, Jing; Ye, Qiongwei; Xia, Yuelong; Yang, Po; Ullah, Amin; Muhammad, Khan

doi:10.1016/j.asoc.2021.107242

Cited by 11 publications

(3 citation statements)

References 48 publications

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“…The DTL can extract relevant features and perform knowledge transfer between the target and source domains. According to the knowledge transfer, transfer learning techniques can be classified into four categories [133], [138]- [141]: Instancesbased DTL [142]- [144], Feature-based DTL [46], [145], [146], Network-based DTL [147]- [149] and Adversarialbased DTL [150]- [153]. Instance-based DTL consists in re-weighting samples from the source field for target field tasks.…”

Section: E Deep Transfer Learning Based Fault Diagnosismentioning

confidence: 99%

Deep Learning-Based Fault Diagnosis of Photovoltaic Systems: A Comprehensive Review and Enhancement Prospects

Mansouri¹,

Trabelsi

Nounou

et al. 2021

IEEE Access

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Photovoltaic (PV) systems are subject to failures during their operation due to the aging effects and external/environmental conditions. These faults may affect the different system components such as PV modules, connection lines, converters/inverters, which can lead to a decrease in the efficiency, performance, and further system collapse. Thus, a key factor to be taken into consideration in high-efficiency grid-connected PV systems is the fault detection and diagnosis (FDD). The performance of the FDD method depends mainly on the quality of the extracted features including real-time changes, phase changes, trend changes, and faulty modes. Thus, the data representation learning is the core stage of intelligent FDD techniques. Recently, due to the enhancement of computing capabilities, the increase of the big data use, and the development of effective algorithms, the deep learning (DL) tool has witnessed a great success in data science. Therefore, this paper proposes an extensive review on deep learning based FDD methods for PV systems. After a brief description of the DL-based strategies, techniques for diagnosing PV systems proposed in recent literature are overviewed and analyzed to point out their differences, advantages and limits. Future research directions towards the improvement of the performance of the DL-based FDD techniques are also discussed. This review paper aims to systematically present the development of DLbased FDD for PV systems and provide guidelines for future research in the field.

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Section: E Deep Transfer Learning Based Fault Diagnosismentioning

confidence: 99%

Deep Learning-Based Fault Diagnosis of Photovoltaic Systems: A Comprehensive Review and Enhancement Prospects

Mansouri¹,

Trabelsi

Nounou

et al. 2021

IEEE Access

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show abstract

“…SanaUllah et al have proposed a new deep learning framework using transfer learning for the detection and classification of breast cancer in breast cytology images and the proposed deep learning framework achieved good classification results [40]. Yun yang et al have proposed a new weighted multi‐feature hybrid transfer learning framework that builds a transformative approach to connect different domains and applied it to healthcare decision making [41]. In the field of biomedical hyperspectral, the current literature rarely reports the use of transfer learning techniques for analysing and processing medical hyperspectral images.…”

Section: Introductionmentioning

confidence: 99%

Automatic detection of ischemic necrotic sites in small intestinal tissue using hyperspectral imaging and transfer learning

Zhang,

Xue,

Xie

et al. 2023

Journal of Biophotonics

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Acquiring large amounts of hyperspectral data of small intestinal tissue with real labels in the clinic is difficult, and the data shows inter‐patient variability. Building an automatic identification model using a small dataset presents a crucial challenge in obtaining a strong generalization of the model. This study aimed to explore the performance of hyperspectral imaging and transfer learning techniques in the automatic identification of normal and ischemic necrotic sites in small intestinal tissue. Hyperspectral data of small intestinal tissues were collected from eight white rabbit samples. The transfer component analysis (TCA) method was performed to transfer learning on hyperspectral data between different samples and the variability of data distribution between samples was reduced. The results showed that the TCA transfer learning method improved the accuracy of the classification model with less training data. This study provided a reliable method for single‐sample modelling to detect necrotic sites in small intestinal tissue .

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“…While supervised learning can solve a lot of significant problems, it also has some limitations. In such an environment, Transfer Learning (TL) becomes particularly important [12,13]. Transfer learning (TL) algorithms for medical image classification are currently widely discussed, such as the magnetic resonance imaging (MRI) dataset.…”

mentioning

confidence: 99%

Inadequate dataset learning for major depressive disorder MRI semantic classification

Liu

Dey²,

Crespo

et al. 2022

IET Image Processing

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A weighted multi-feature transfer learning framework for intelligent medical decision making

Cited by 11 publications

References 48 publications

Deep Learning-Based Fault Diagnosis of Photovoltaic Systems: A Comprehensive Review and Enhancement Prospects

Deep Learning-Based Fault Diagnosis of Photovoltaic Systems: A Comprehensive Review and Enhancement Prospects

Automatic detection of ischemic necrotic sites in small intestinal tissue using hyperspectral imaging and transfer learning

Inadequate dataset learning for major depressive disorder MRI semantic classification

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