Study on the improved fuzzy clustering algorithm and its application in brain image segmentation

Ren, Tianbao; Wang, Huanhuan; Feng, Hao; Xu, Chunxiang; Guoshun, Liu; Ding, Pan

doi:10.1016/j.asoc.2019.105503

Cited by 63 publications

(28 citation statements)

References 31 publications

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“…Every particular point in the dataset (location in our dataset) is a member of at least two clusters with unlike weights. Moreover, the clustering approach can uncover concealed information or previously undetected relationships between input data [23,24].…”

Section: Clustering the Poi'smentioning

confidence: 99%

RecPOID: POI Recommendation with Friendship Aware and Deep CNN

Safavi

Jalali

2021

Future Internet

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In location-based social networks (LBSNs), exploit several key features of points-of-interest (POIs) and users on precise POI recommendation be significant. In this work, a novel POI recommendation pipeline based on the convolutional neural network named RecPOID is proposed, which can recommend an accurate sequence of top-k POIs and considers only the effect of the most similar pattern friendship rather than all user’s friendship. We use the fuzzy c-mean clustering method to find the similarity. Temporal and spatial features of similar friends are fed to our Deep CNN model. The 10-layer convolutional neural network can predict longitude and latitude and the Id of the next proper locations; after that, based on the shortest time distance from a similar pattern’s friendship, select the smallest distance locations. The proposed structure uses six features, including user’s ID, month, day, hour, minute, and second of visiting time by each user as inputs. RecPOID based on two accessible LBSNs datasets is evaluated. Experimental outcomes illustrate considering most similar friendship could improve the accuracy of recommendations and the proposed RecPOID for POI recommendation outperforms state-of-the-art approaches.

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Section: Clustering the Poi'smentioning

confidence: 99%

RecPOID: POI Recommendation with Friendship Aware and Deep CNN

Safavi

Jalali

2021

Future Internet

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“…However, external forces cause the curve to be mapped into the more desirable areas of the image (edges). Based on curve and surface representation, the deformable models are divided into parametric (active contour) and non‐parametric groups [20].…”

Section: Related Workmentioning

confidence: 99%

Infant brain segmentation based on a combination of VGG‐16 and U‐Net deep neural networks

et al. 2020

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Medical image segmentation plays a key role in identifying the disease type. In the last decade, various methods have been proposed for medical images segmentation. Despite many efforts made in medical imaging, segmentation of medical images still faces challenges, concerning the variety of shape, location, and texture quality. According to recent studies and magnetic resonance imaging, segmentation of brain images at around 6 months of age is a challenging issue in brain image segmentation due to low tissue contrast between white matter (WM) and grey matter (GM) regions. In this study, using the deep learning model, the convolutional network for the brain fragmentation is presented. First, the image quality is improved using the pre‐processing method. The number of layers utilised in the proposed method is less than that of known models. In the pooling layer, instead of using the maximum function, the averaging function is employed. Sixty‐four batches are also considered to improve the performance of the proposed method. The method is evaluated on the iSeg‐2017 database. The DISC and ASC measures of the proposed method for the three classes of GM, WM, and cerebrovascular fluid are 0.902, 0.594, 0.930, 0.481, 0.971, and 0.231, respectively.

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“…T. Ren et al [9] proposed the method to solve brain tumor segmentation. Initially, the irrelevant information is removed from the image by histogram equalization.…”

Section: Literature Surveymentioning

confidence: 99%

A Framework for Brain Tumor Segmentation and Classification using Deep Learning Algorithm

Kulkarni¹,

Sundari²

2020

IJACSA

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The brain tumor is a cluster of the abnormal tissues, and it is essential to categorize brain tumors for treatment using Magnetic Resonance Imaging (MRI). The segmentation of tumors from brain MRI is understood to be complicated and also crucial tasks. It can be further use in surgery, medical preparation, and assessments. In addition to this, the brain MRI classification is also essential. The enhancement of machine learning and technology will aid radiologists in diagnosing tumors without taking invasive steps. In this paper, the method to detect a brain tumor and classification has been present. Brain tumor detection processes through pre-processing, skull stripping, and tumor segmentation. It is employing a thresholding method followed by morphological operations. The number of training image influences the feature extracted by the CNN, then CNN models overfit after some epoch. Hence, deep learning CNN with transfer learning techniques has evolved. The tumorous brain MRI is classified using CNN based AlexNet architecture. Further, the malignant brain tumor is classified using GooLeNet transfer learning architecture. The performance of this approach is evaluated by precision, recall, F-measure, and accuracy metrics.

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Study on the improved fuzzy clustering algorithm and its application in brain image segmentation

Cited by 63 publications

References 31 publications

RecPOID: POI Recommendation with Friendship Aware and Deep CNN

RecPOID: POI Recommendation with Friendship Aware and Deep CNN

Infant brain segmentation based on a combination of VGG‐16 and U‐Net deep neural networks

A Framework for Brain Tumor Segmentation and Classification using Deep Learning Algorithm

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