Brain tumor detection by using a modified FCM and Level set algorithms

Beddad, Boucif; Hachemi, Kaddour

doi:10.1109/ceit.2016.7929114

Cited by 10 publications

(2 citation statements)

References 9 publications

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“…Usage of sophisticated image capturing devices was introduced in the study of Baghaei et al [11] for identification of lesions from the phantom brain images that has exhibited the capability to identify less than 7 mm size of lesions. Similar direction of the study towards detection process was also carried out by Beddad and Hachemi [12] who have used fuzzy clustering method for classification along with the level set algorithm. Segmentation has been marked as role player in majority of the existing studies.…”

Section: Introductionmentioning

confidence: 84%

A novel framework for efficient identification of brain cancer region from brain MRI

Angadi¹,

Nagendra²,

Hanumanthappa³

2019

IJECE

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Diagnosis of brain cancer using existing imaging techniques, e.g., Magnetic Resonance Imaging (MRI) is shrouded with various degrees of challenges. At present, there are very few significant research models focusing on introducing some novel and unique solutions towards such problems of detection. Moreover, existing techniques are found to have lesser accuracy as compared to other detection schemes. Therefore, the proposed paper presents a framework that introduces a series of simple and computationally cost-effective techniques that have assisted in leveraging the accuracy level to a very higher degree. The proposed framework takes the input image and subjects it to non-conventional segmentation mechanism followed by optimizing the performance using directed acyclic graph, Bayesian Network, and neural network. The study outcome of the proposed system shows the significantly higher degree of accuracy in detection performance as compared to frequently existing approaches.

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Section: Introductionmentioning

confidence: 84%

A novel framework for efficient identification of brain cancer region from brain MRI

Angadi¹,

Nagendra²,

Hanumanthappa³

2019

IJECE

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“…This method has been proposed along with the level set method in [2]. In this unsupervised method, clustering was achieved by the k-mean algorithm.…”

Section: H Fuzzy C-mean (Fcm)mentioning

confidence: 99%

Machine Learning Algorithm for Early Detection and Analysis of Brain Tumors Using MRI Images

Terdale

Bhole

Bhor

et al. 2023

IJRITCC

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Among the human body's organs, the brain is the most delicate and specialized. It is proven that after the heart stops then also brain death occurs within 3 to 5 minutes of death or within 3 to 5 minutes of loss of oxygen supply. A brain tumor is a life-threatening disease that can be detected at any age from an infant to an old person. Though a lot of people did research in the detection and analysis of a tumor, but then also detecting tumors at the early phase is still a much more arduous field in the biomedical study. This paper focuses on the comparative study of various existing algorithms in this field. This paper addresses the challenges and some issues in MRI brain tumor detection which are also addressed in this research.

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