Image Analysis for MRI Based Brain Tumor Detection and Feature Extraction Using Biologically Inspired BWT and SVM

Bahadure, Nilesh Bhaskarrao; Ray, Arun Kumar; Thethi, H. Pal

doi:10.1155/2017/9749108

Cited by 429 publications

(162 citation statements)

References 30 publications

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“…Anitha et al used CNN classification approach for classifying the Glioma brain tumors from normal brain MRI images and obtained 96.1% of sensitivity, 97.2% of specificity, and 98.1% of accuracy. Nilesh Bhaskarrao Bahadure et al used SVM classification approach for detecting the abnormal regions in brain MRI images using SVM classification algorithm. The authors obtained 95.2% of sensitivity, 96.5% of specificity, and 97.1% of accuracy.…”

Section: Resultsmentioning

confidence: 99%

“…The authors applied their proposed algorithm on BRATS open access dataset. Nilesh Bhaskarrao Bahadure et al used support vector machine (SVM) classification approach for detecting the abnormal regions in brain MRI images using SVM classification algorithm. The variable image description features such as shape and size of the different tumor regions were trained by SVM approach.…”

Section: Literature Surveymentioning

confidence: 99%

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Computer aided automated detection and classification of brain tumors using CANFIS classification method

Johnpeter¹,

Ponnuchamy²

2019

Int J Imaging Syst Tech

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The development of abnormal cells in human brain leads to the formation of tumors. This article proposes an efficient approach for brain tumor detection and segmentation using image fusion and co‐active adaptive neuro fuzzy inference system (CANFIS) classification method. The brain MRI images are fused and the dual tree complex wavelet transform is applied on the fused image. Then, the statistical features, local ternary pattern features and gray level co‐occurrence matrix features. These extracted features are classified using CANFIS classification approach for the classification of source brain MRI image into either normal or abnormal. Further, morphological operations are applied on the abnormal brain MRI image for segmenting the tumor regions. The proposed methodology is evaluated with respect to the performance metrics sensitivity, specificity, positive predictive value, negative predictive value, tumor segmentation accuracy with detection rate. The proposed image fusion based brain tumor detection and classification methodology stated in this article achieves 96.5% of average sensitivity, 97.7% of average specificity, 87.6% of positive predictive value, 96.6% of negative predictive value, and 98.8% of average accuracy.

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Section: Resultsmentioning

confidence: 99%

Section: Literature Surveymentioning

confidence: 99%

Computer aided automated detection and classification of brain tumors using CANFIS classification method

Johnpeter¹,

Ponnuchamy²

2019

Int J Imaging Syst Tech

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“…Bahadure et al decomposed the source brain images using Berkeley wavelet transformation approach and then the decomposed coefficients were trained and classified using support vector machine (SVM) classification algorithm. The authors obtained 97.72% of sensitivity, 94.2% of specificity and 96.51% of tumor region segmentation accuracy with respect to its corresponding ground truth images.…”

Section: Discussionmentioning

confidence: 99%

Image registration‐based brain tumor detection and segmentation using ANFIS classification approach

Nagarathinam¹,

Ponnuchamy

2019

Int J Imaging Syst Tech

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Abnormal cells in human brain lead to the development of tumors. Manual detection of this tumor region is a time‐consuming process. Hence, this paper proposes an efficient and automated computer‐aided methodology for brain tumor detection and segmentation using image registration technique and classification approaches. This proposed work consists of the following modules: image registration, contourlet transform, and feature extraction with feature normalization, classification, and segmentation. The extracted features are optimized using genetic algorithm, and then an adaptive neuro‐fuzzy inference system classification approach is used to classify the features for the detection and segmentation of tumor regions in brain magnetic resonance imaging. A quantitative analysis is performed to evaluate the proposed methodology for brain tumor detection using sensitivity, specificity, segmentation accuracy, precision, and Dice similarity coefficient.

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“…The mobile apps can also be exhibited as icons to help those with reading difficulties. The system can also be multilingual [57,58]. A brief description of various subsystems or mobile applications follows.…”

Section: Proposed Healthcare Management System (Hms)mentioning

confidence: 99%

Managing Crowds with Wireless and Mobile Technologies

Yamin

Basahel

Sen

2018

Wireless Communications and Mobile Computing

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Thousands of people have lost their lives in stampedes and other crowd related disasters in recent years. Most of these fatalities seem to have been caused by poor control and management of crowds, which is discussed in this article. An efficient and effective crowd management system must also have a plan to deal with the ongoing threat of terrorism and outbreak of various kinds of communicable diseases. In this article, we present a framework of a Crowd Control and Health Management System specially designed to prevent and manage stampedes and other disasters. The system has two subsystems; one for dealing with the management of stampedes and other disasters and the other with healthcare management. As part of the proposed system, an algorithm for an early detection of stampedes, with proof and simulation of implementation, is provided. As part of the healthcare management subsystem, we integrate several mobile applications and develop four of them dealing with relief issues, blood donations, complaints and alerts, and utilizing mobile phones as a sensor device. Our system makes use of various kinds of wireless, mobile, and other technologies and tools including Fog Computing, Smart Phones, Smart Digital Street, IP-Cameras, Radio Frequency Identification (RFID), Voice Alarm, Light Alarm, and Global Positioning System (GPS). We compare merits and effectiveness of RFID and Wireless Sensor Networks (WSNs), as well as those of Cloud and Fog with a view of using them as part of the proposed framework. We also discuss applications of our systems in real-life cases of Hajj, an annual pilgrimage of millions of people to Mecca, and Kumbh Mela, a periodic gathering of tens of millions of people in India, both of which have accounted for the majority of fatalities in stampedes and other disasters.

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Image Analysis for MRI Based Brain Tumor Detection and Feature Extraction Using Biologically Inspired BWT and SVM

Cited by 429 publications

References 30 publications

Computer aided automated detection and classification of brain tumors using CANFIS classification method

Computer aided automated detection and classification of brain tumors using CANFIS classification method

Image registration‐based brain tumor detection and segmentation using ANFIS classification approach

Managing Crowds with Wireless and Mobile Technologies

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