RETRACTED ARTICLE: Brain Tumor Segmentation Using Deep Learning and Fuzzy K-Means Clustering for Magnetic Resonance Images

Pitchai, R.; Supraja, P.; Victoria, A. Helen; Madhavi, Maulik C.

doi:10.1007/s11063-020-10326-4

Cited by 73 publications

(21 citation statements)

References 18 publications

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“…Table 1. Compression accuracy rate between proposed methods and other methods Authors Technique Accuracy rate (%) [28] Intelligent mean shfit 92 [20] Contrast corection 89 [29] Improved fuzzy and watershed 88 [30] Fuzzy and K-mean 86 [31] Fuzzy and C-mean 85 Proposed method Hybrid image processing technique 95…”

Section: Resultsmentioning

confidence: 99%

Automated brain tumor detection of MRI image based on hybrid image processing techniques

2022

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Primary challenges are the identification, segmentation, and extraction of the afflicted area from the scanning of magnetic resonance. However, it is a time-consuming and tiresome for clinical specialists. In this paper, an automated brain tumor system is proposed. The proposed system employs hybrid image processing techniques such as contrast correction, histogram normalization, thresholding techniques, arithmetic, and morphological operations to quarantine nearby organs and other tissue from the brain for improving the localization of the affected region. At first, the skull stripping process is proposed to segregate the non-designated regions to extract the designated brain regions. Those resultant brain region images are further subjected to discover the brain tumor. The planned scheme is studied on the magnetic resonance (MR) images with the use of T1, T2, T1c, and fluid-attenuated inversion recovery (FLAIR). The proposed hybrid method employed. The results reveal that the proposed method is quite efficient to extract the tumor region. The accuracy rate for segmentation and separation of area of interest in brain tumor reached to 95%. Finally, the significance of the proposed procedure is confirmed using the real image clinical dataset got from ten patients were diagnosed as begin, malignant, and metastatic brain tumors in Al-Yarmouk and Baghdad teaching hospital in Baghdad, Iraq.

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Section: Resultsmentioning

confidence: 99%

Automated brain tumor detection of MRI image based on hybrid image processing techniques

2022

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“…The performance of any application can be valued by validating the chief metrics like sensitivity, accuracy, f ‐measure, precision, and execution time. Here, to know the improvement measure of the tumor segmentation system from the existing models, some comparisons were made with old schemes like Fuzzy k‐means model (FKM), 25 Optimized Laplacian (OL), 26 Component‐Analysis and Linear Discriminate Independent Model (CALDIM), 27 and Supervised & Un‐Supervised Learning (S‐USL) 28 …”

Section: Resultsmentioning

confidence: 99%

An automated brain tumor segmentation framework using a novel fruit fly UNet

Boda¹,

Cherian

Kumar

2022

Concurrency and Computation

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Summary Brain image analysis and segmentation are the most difficult tasks in medical image processing because of image complexity. Moreover, MRI images are mostly utilized to predict different brain‐based diseases; if the images are complex, the disease prediction accuracy is very low. To overcome this problem, the current research has planned to design a novel fruit fly‐based UNet (FFbU) framework to detect the Tumor accurately. Moreover, the fitness of the fruit fly was upgraded in the UNet pooling module that has tended to gain the finest results. Initially, the standard datasets were gathered from the net source and trained to the system. Consequently, the training error is removed in the primary layer of FFbU then the error‐cleared data is entered into the UNet dense layer for tumor detection and segmentation. Finally, the proposed model is executed in a MATLAB environment, and the proficiency of the designed FFbU model is estimated in terms of accuracy, recall, precision, Dice, and Jaccard. In addition, the planned novel FFbU model has the ability to predict and segment different tumor types.

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“…Clustering techniques, which are an unsupervised learning method, have been widely investigated in medical image segmentation. However, in this survey work some of the most popular clustering methods, such as k-means and its varieties [ 38 , 39 , 40 , 41 , 42 , 43 , 44 ], fuzzy c-means [ 38 , 39 , 41 , 45 ], subtractive clustering (SC), and hybrid techniques [ 46 , 47 , 48 ].…”

Section: Brain Tumor Segmentation Methodsmentioning

confidence: 99%

A Survey of Brain Tumor Segmentation and Classification Algorithms

Biratu

Schwenker

Ayano³

et al. 2021

J. Imaging

106

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A brain Magnetic resonance imaging (MRI) scan of a single individual consists of several slices across the 3D anatomical view. Therefore, manual segmentation of brain tumors from magnetic resonance (MR) images is a challenging and time-consuming task. In addition, an automated brain tumor classification from an MRI scan is non-invasive so that it avoids biopsy and make the diagnosis process safer. Since the beginning of this millennia and late nineties, the effort of the research community to come-up with automatic brain tumor segmentation and classification method has been tremendous. As a result, there are ample literature on the area focusing on segmentation using region growing, traditional machine learning and deep learning methods. Similarly, a number of tasks have been performed in the area of brain tumor classification into their respective histological type, and an impressive performance results have been obtained. Considering state of-the-art methods and their performance, the purpose of this paper is to provide a comprehensive survey of three, recently proposed, major brain tumor segmentation and classification model techniques, namely, region growing, shallow machine learning and deep learning. The established works included in this survey also covers technical aspects such as the strengths and weaknesses of different approaches, pre- and post-processing techniques, feature extraction, datasets, and models’ performance evaluation metrics.

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RETRACTED ARTICLE: Brain Tumor Segmentation Using Deep Learning and Fuzzy K-Means Clustering for Magnetic Resonance Images

Cited by 73 publications

References 18 publications

Automated brain tumor detection of MRI image based on hybrid image processing techniques

Automated brain tumor detection of MRI image based on hybrid image processing techniques

An automated brain tumor segmentation framework using a novel fruit fly UNet

A Survey of Brain Tumor Segmentation and Classification Algorithms

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