DNN-Based Brain MRI Classification Using Fuzzy Clustering and Autoencoder Features

Chauhan, Nishant; Choi, Byung-Jae

doi:10.5391/ijfis.2021.21.4.349

Cited by 5 publications

(5 citation statements)

References 21 publications

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“…The level set method and fuzzy C-means clustering algorithm are used to segment liver lesions, and =stacked sparse AE is assigned to extract highlevel feature representation from pixels of the segmented images (Hassan, Elmogy et al 2017). Similarly, fuzzy C-means is used to segment brain MRI, and the segmented image is adopted as input data of AE for feature reduction (Chauhan and Choi 2021). A hybrid depth acoustic emission segmentation method based on Bayesian fuzzy clustering is proposed for brain tumor classification (Raja and Rani 2020).…”

Section: Fuzzy Autoencodermentioning

confidence: 99%

“…Furthermore, fuzzy C-means method is executed for clustering unlabeled data, and then, the clustering data is input into the deep neural network to form the hybrid model (Joloudari, Saadatfar et al 2022). Fuzzy C-means method or fuzzy gray level co-occurrence matrix is used for image segmentation, and the segmented images are delivered to autoencoder or convolutional neural network to feature representation or feature reduction (Hassan, Elmogy et al 2017, Chauhan and Choi 2021, Yamunadevi and Ranjani 2021. Secondly, the simultaneous framework of fuzzy deep learning methods is shown in Figure 6.…”

Section: Fuzzy Deep Learning For Uncertain Medical Datamentioning

confidence: 99%

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A Systematic Survey of Fuzzy Deep Learning for Uncertain Medical Data

Zheng

Zhang

et al. 2023

Preprint

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Intelligent medical industry is in a rapid stage of development around the world, followed by are the expanding market size and basic theories of intelligent medical diagnosis and decision-making. Deep learning models have achieved good practical results in medical domain. However, traditional deep learning is almost calculated and developed by crisp values, while imprecise, uncertain, and vague medical data is common in the process of diagnosis and treatment. Medical data is usually uncertain because of the presence of noise, artifact or high dimensional unstructured information in the data. Fortunately, fuzzy deep learning that originated from fuzzy sets, can effectively deal with uncertain and inaccurate information, providing new viewpoints for alleviating the problems above. Therefore, it is important and necessary to review the contributions of fuzzy deep learning for uncertain medical data in recent years. This paper first constructs four types of frameworks of fuzzy deep learning methods used for uncertain medical data, and investigates the status from three aspects: fuzzy deep learning methods, type of uncertain medical data and execution effects. Then the performance evaluation metrics of fuzzy deep learning models are analyzed in details. We have found that although fuzzy deep learning indeed develops the interpretability, low-quality images, boundary point processing, imprecise text records processing and multi-source heterogenous data fusion in medical domain, challenges still exist and future research directions are provided from the perspective of genetic data processing, intelligent medical decision-making based on complex cognition information, health management of Traditional Chinese Medical and few-shot learning.

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Section: Fuzzy Autoencodermentioning

confidence: 99%

Section: Fuzzy Deep Learning For Uncertain Medical Datamentioning

confidence: 99%

A Systematic Survey of Fuzzy Deep Learning for Uncertain Medical Data

Zheng

Zhang

et al. 2023

Preprint

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“…The purpose of feature selection is to select the right features in order to get a better understanding of the characteristics of the data. Therefore, selecting significant features will assist the model in studying the data and producing the right label [23]. Feature selection can also enhance classification accuracy [29] [30].…”

Section: A Features Selection and Weightingmentioning

confidence: 99%

“…Feature-based also can be improved the classification accuracy when it is used with the classifier like SVM [19] [20] [21]. However, one of the weakness of SVM is restricted the www.ijacsa.thesai.org data precision and requires computational cost, so it needs an additional step, such as features reduction to minimize cost [22] [23]. Many techniques can be applied in feature-transfer learning, for example, the use of the Grassman manifold as the geometric property [16], adding balance unit parameter to overcome the data imbalance problem [9], or the formation of subspace features to minimize the distribution difference [17].…”

Section: Introductionmentioning

confidence: 99%

A Feature-based Transfer Learning to Improve the Image Classification with Support Vector Machine

Sevani¹,

Azizah²,

Jatmiko³

2023

IJACSA

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In the big data era there are some issues regarding real-world classification problems. Some of the important challenges that still need to be overcome to produce an accurate classification model are the data imbalance, difficulties in labeling process, and differences on data distribution. Most classification problems are related to the differences in the data distribution and the lack of labels on some datasets while other datasets have abundant labels. To address the problem, this paper proposes a weighted-based feature-transfer learning (WbFTL) method to transfer knowledge between different but related domains, called cross-domain. The knowledge transfer is done through making a new feature representations in order to reduce the cross-domain's distribution differences while maintaining the local structure of the domain. To make the new feature representation we implement a feature selection and inter-cluster class distance. We propose two stages of the feature selection process to capture the knowledge of the feature and its relation to the label. The first stage uses a threshold to select the feature. The second stage uses ANOVA (Analysis of Variance) to select features that are significant to the label. To enhance the accuracy, the selected features are weighted before being used for the training process using SVM. The proposed WbFTL are compared to 1-NN and PCA as baseline 1 and baseline 2. Both baseline models represent the traditional machine learning and dimensionality reduction method, without implementing transfer learning. It is also compared with TCA, the first feature-transfer learning work on this same task, as baseline 3. The experiment results of 12 cross-domain tasks on Office and Caltech dataset show that the proposed WbFTL can increase the average accuracy by 15.25%, 6.83%, and 3.59% compared to baseline 1, baseline 2, and baseline 3, respectively.

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“…The generator captures the data distribution and the discriminator estimates the probability related to the training data. Various models based on GANs have been implemented for augmenting images such as CycleGAN and StarGAN [16][17][18][19][20].…”

Section: Data Augmentationmentioning

confidence: 99%

Anomaly Detection Using Puzzle-Based Data Augmentation to Overcome Data Imbalances and Deficiencies

Kim,

Jung,

Kim

et al. 2023

Machines

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Machine tools are used in a wide range of applications, and they can manufacture workpieces flexibly. Furthermore, they require maintenance; the overall costs include maintenance costs, which constitute a significant portion, and the costs involved in ensuring product quality. Therefore, anomaly detection in tool conditions is required, because these tools are essential industrial elements. However, the data related to tool conditions present some challenges: data imbalances and deficiencies. Data imbalances and deficiencies can affect the performance of anomaly detection models. A model trained using data with imbalances and deficiencies may miscalculate that abnormal data are normal data, leasing to errors. To overcome these problems, the proposed method has been designed using the wavelet transform, color space conversion, color extraction, puzzle-based data augmentation, and double transfer learning. The proposed method generated image data from time-series data, effectively extracted features, and generated new image data using puzzle-based data augmentation. The color information was processed to highlight features, and the proposed puzzle-based data augmentation was applied during processing to increase the amount of data to improve the performance of the anomaly detection model. The experimental results showed that the proposed method can classify normal and abnormal data with greater accuracy. In particular, the accuracy of abnormal data classification increased from 25.00% to 91.67%. This demonstrates that the proposed method is effective and can overcome data imbalances and deficiencies.

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DNN-Based Brain MRI Classification Using Fuzzy Clustering and Autoencoder Features

Cited by 5 publications

References 21 publications

A Systematic Survey of Fuzzy Deep Learning for Uncertain Medical Data

A Systematic Survey of Fuzzy Deep Learning for Uncertain Medical Data

A Feature-based Transfer Learning to Improve the Image Classification with Support Vector Machine

Anomaly Detection Using Puzzle-Based Data Augmentation to Overcome Data Imbalances and Deficiencies

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