Detection of tumor in brain MRI using fuzzy feature selection and support vector machine

Halder, Amiya; Dobe, Oyendrila

doi:10.1109/icacci.2016.7732331

Cited by 11 publications

(7 citation statements)

References 14 publications

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“…Figure 8 shows the relationship between the precision and the recall when R takes different values, where =3 and =0.8. The experiment takes R= [1,10]. Usually, we hope that the higher the precision of retrieval results, the better the recall, but in fact, the two are contradictory in some cases.…”

Section: Effect On Number Of Tagsmentioning

confidence: 99%

“…In contrast to the content-based algorithms, model-based algorithms [5][6][7][8][9] often use machine learning to solve the problem of semantic annotation. Broadly speaking, machine learning gives machine learning ability, which plays an important role in the identification of human diseases [10], classification of products [11], feature selection [12] and image processing [13].…”

Section: Introductionmentioning

confidence: 99%

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Random Walk-Based Semantic Annotation for On-demand Printing Products

Zhang¹,

Su²

2019

AJNNA

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Nowadays, the scale of real network is increasing day by day, while also brings sparse problems. It is usually necessary to maintain a large number of product information. To organize this product information, a feasible way is to add semantic tags to the information. In this article, we aim to solve the problem of semantic annotation of on-demand printing products. Based on good properties of random walk in global networks, we deal with the sparsity problem by applying it, and then propose an efficient ProRWR algorithm. Firstly, it processes the text description dataset of printed products based on TF-IDF algorithm, and builds "product-term" bipartite network. Secondly, ProRWR builds square matrix using the TF-IDF weight matrix, rewrite the equation of random walk, and use the normalized square matrix as the input of rewrite ProRWR algorithm. By random walks, terms with the highest convergence probability in each product document are selected as the most relevant feature terms of the product. A large number of experiments have been done on Amazon dataset. The results show that the precision and recall of our algorithm are 73.5% and 60%, respectively, indicating that ProRWR has discovered the potential semantic association and implemented the semantic annotation of on-demand printed products.

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Section: Effect On Number Of Tagsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Random Walk-Based Semantic Annotation for On-demand Printing Products

Zhang¹,

Su²

2019

AJNNA

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“…The obtained result gave 88% of accuracy for 25 MRI images which is not satisfactory. Amiya Halder et al [24] worked on "Detection of Tumor in Brain MRI Using Fuzzy Feature Selection and Support Vector Machine" where method is divided into two steps. First, a set of feature is generated for accurately differentiating between a normal and abnormal MR scan images.…”

Section: Literature Reviewmentioning

confidence: 99%

A Review on Performance Evaluation of Image Processing Technique for Brain Tumor Detection

2017

IJMTER

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Brain tumor is an uncontrolled growth of tissues in any part of the body. Tumors are of different types and characteristics and have different treatments. Detection of tumor in the earlier stages makes the treatment easier. Presently many medical imaging techniques such as positron emission tomography (PET), X-ray, computed tomography (CT), magnetic resonance imaging (MRI) for tumor detection are in use among which MRI imaging technique is more attractive because of its higher resolution and therefore most preferred technique for diagnosing tumor. MRI scan is safer than any other scan as it does not involve any radiations and gives high resolution of image. Paper gives a brief review of different segmentation and classification methods used for detection of tumor from Magnetic Resonance Images (MRI).

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“…Kavetha.BV, Venu Gopala Krishnan.J, et.al [10] used CVPartition method for classifying, deciding and detecting Maligant and Benign in mammorgams. Amiya Halder, Oyendrila Dobe et.al [17] explained about Fuzzy feature selection and support vector machine for detecting Tumor in Brain MRI.…”

Section: Related Workmentioning

confidence: 99%