Performance analysis of classifier for brain tumor detection and diagnosis

Shanthakumar, P.; Ganeshkumar, P.

doi:10.1016/j.compeleceng.2015.05.011

Cited by 41 publications

(16 citation statements)

References 14 publications

(16 reference statements)

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“…But the detections are found to be consist of discrete pixels in thecase of threshold method of segmentation which can be discontinuous and incomplete. Also, this type of image processing technique is comparatively more expensive than other methods [12].…”

Section: Threshold Methodsmentioning

confidence: 99%

“…The achieved results are quantitatively compared and evaluated with numerous ground truth data. The proposed technique gives fast and improved segmentation with high classification rate by producing 99.4 percent of sensitivity, 97.03 percent of positive predictive value, 99.6 percent of specificity, and 99.5 percent of total accuracy [12].…”

Section: P Shanthakumar and P Ganeshkumar (2014)mentioning

confidence: 99%

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Comparative Review of Image Denoising and Segmentation Approaches For Detection of Tumor in Brain Images

Kaur¹

2017

IJRASET

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A group of defective cells that grow inside or around the brain referred as Barin Tumor. The number of Brain Tumor cases around the World is increasing day by day. So, it is significant to detect it at anearly stage. Segmentation of brain images holds thesignificant part for detection of Tumor brain. Manual segmentation of brain Tumor tissues cannot be compared with existing high-speed computing machines. Therefore, theemphasisis given on computer aided detection of brain Tumor. This paper provides an overview of various techniques that have been implemented till now to detect the brain tumor. Along with this, these techniques are compared for accuracy, sensitivity, and specificity to detect the Brain tumor. The research findings will help to explore the research in the same field.

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Section: Threshold Methodsmentioning

confidence: 99%

Section: P Shanthakumar and P Ganeshkumar (2014)mentioning

confidence: 99%

Comparative Review of Image Denoising and Segmentation Approaches For Detection of Tumor in Brain Images

Kaur¹

2017

IJRASET

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“…Patients often attend their GP practice on multiple occasions (in 2013, 38% of brain tumor patients reported to their GP more than five times prior to diagnosis) (The Brain Tumour Charity, 2014), and are then referred for magnetic resonance imaging (MRI) and/or computed tomography (CT). These results are then confirmed by histological assessment of a surgical specimen following a brain biopsy; however, diagnosis is often made too late for curative therapy (Bashour et al 2015;Shanthakumar and Ganeshkumar 2015). A methodology capable of early diagnosis or being able to monitor high-risk patients would prove to be an extremely valuable tool.…”

Section: Introductionmentioning

confidence: 99%

A review of novel analytical diagnostics for liquid biopsies: spectroscopic and spectrometric serum profiling of primary and secondary brain tumors

Spalding

Board²,

Dawson

et al. 2016

Brain and Behavior

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IntroductionSpectroscopic and spectrometric analysis of biological samples is regarded as quick, cost effective, easy to operate, and spectroscopic sample preparation involves minimal sample preparation.ResultsTechniques like infrared (IR) spectroscopy, surface‐enhanced laser desorption/ionization (SELDI)‐mass spectroscopy (MS), and matrix‐assisted laser desorption/ionization (MALDI) ‐MS could enable early diagnosis of cancer, disease monitoring, and assessment of treatment responses allowing refinement, if required.DiscussionCarrying out analytical testing within outpatient clinics would dramatically cut the time spent by patients attending different appointments, at different locations, save hospital time and resources but importantly would theoretically enable a reduction in mortality and morbidity. While the advantages of such a prospect seem obvious, this review aims to evaluate the use of human serum spectroscopic and spectrometric analysis as a diagnostic tool for brain cancers, creating a platform for the future of cancer diagnostics.

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“…The filter ranks the neigh hood pixels based on their intensity values, to determine the median value for pixel under evaluation the central pixel is then replaced by the new middle value. One of the advantages of Anisotropic diffusion filter is it gives the best result for short or impulse type of noise, for extremely high values too [6]. …”

Section: Pre-processingmentioning

confidence: 99%

“…Anisotropic filtering used for pre-processing [6]: The MRI images of the Brain are subjected to different type of noise during image transmission and also during the digitization process. Pre-Processing is the first and the most important step involved in segmentation and detection of the tumors.…”

Section: Pre-processingmentioning

confidence: 99%

Expounding the MRI Sequences for Computer Aided Diagnosis for Detection of Brain Tumors

Shinde¹,

Desai²

2017

IJCA

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Abnormal and uncontrollable growth of the cells causes tumors .Early diagnosis by the physician and proper treatment of the tumors are essential for the prevention of permanent damage of the affected area and so also prevents death. The soft tissues of the body get affected by tumors, brain is one of the commonly affected areas with tumor .The Magnetic Resonance Imaging (MRI) is one of the power full techniques mainly used for detection of tumors. It is a radiation-based technique which represents the internal structure of the body in terms of intensity variations that are radiated by the biological system when exposed to Radio Frequency (RF). When the brain images are inspected or interpreted one should be aware of the image contrast since the entire information about brain is mapped into intensity variations of the brain MRI images captured during image acquisition the artifacts introduced affect the quality of analysis the physician also needs the quantification of the tumor area [1] hence it is required to an efficient rectifying methodology for removal of these artifacts present in the image before diagnosis. Here in this paper attempt is made to explain the different Sequences of Brain MRI and also enlighten the different computer aided techniques used for segmentation, and bring forward one of the method for tumor detection after Preprocessing.

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Performance analysis of classifier for brain tumor detection and diagnosis

Cited by 41 publications

References 14 publications

Comparative Review of Image Denoising and Segmentation Approaches For Detection of Tumor in Brain Images

Comparative Review of Image Denoising and Segmentation Approaches For Detection of Tumor in Brain Images

A review of novel analytical diagnostics for liquid biopsies: spectroscopic and spectrometric serum profiling of primary and secondary brain tumors

Expounding the MRI Sequences for Computer Aided Diagnosis for Detection of Brain Tumors

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