Author
Vivek Kapur
Bio: Vivek Kapur is an academic researcher from Rajiv Gandhi College of Engineering. The author has contributed to research in topics: Folded inverted conformal antenna & Support vector machine. The author has an hindex of 2, co-authored 2 publications receiving 83 citations.
Papers
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28 May 2015
TL;DR: An intellectual classification system to recognize normal and abnormal MRI brain images and the Hybrid classifier SVM-KNN demonstrated the highest classification accuracy rate of 98% among others is proposed.
Abstract: This paper proposes an intellectual classification system to recognize normal and abnormal MRI brain images. Nowadays, decision and treatment of brain tumors is based on symptoms and radiological appearance. Magnetic resonance imaging (MRI) is a most important controlled tool for the anatomical judgment of tumors in brain. In the present investigation, various techniques were used for the classification of brain cancer. Under these techniques, image preprocessing, image feature extraction and subsequent classification of brain cancer is successfully performed. When different machine learning techniques: Support Vector Machine (SVM), K- Nearest Neighbor (KNN) and Hybrid Classifier (SVM-KNN) is used to classify 50 images, it is observed from the results that the Hybrid classifier SVM-KNN demonstrated the highest classification accuracy rate of 98% among others. The main goal of this paper is to give an excellent outcome of MRI brain cancer classification rate using SVM-KNN.
113 citations
01 Dec 2015
TL;DR: In this article, an asymmetric Sai shape defected ground structure (DGS) is inserted in a ground plane of a conventional Rectangular Microstrip patch antenna (RMSA) for 2.4 GHz frequency.
Abstract: This paper presents a Bandwidth enhancement of microstrip patch antenna using defected ground structure (DGS). In this paper asymmetric Sai shape defected ground structure (DGS) is inserted in a ground plane of a conventional Rectangular Microstrip patch antenna (RMSA). The antenna is designed for 2.4 GHz frequency. The antenna is simulated by the software HFSS. HFSS is employed to analyze the proposed antenna and simulated results. The Results of simulated antenna and fabricated antenna are compared .The Major focus of this paper is to improve the band width so that patch antenna is used for wide band applications.
6 citations
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TL;DR: A DL model based on a convolutional neural network is proposed to classify different brain tumor types using two publicly available datasets and the results indicate the ability of the model for brain tumor multi-classification purposes.
Abstract: Brain tumor classification is a crucial task to evaluate the tumors and make a treatment decision according to their classes. There are many imaging techniques used to detect brain tumors. However, MRI is commonly used due to its superior image quality and the fact of relying on no ionizing radiation. Deep learning (DL) is a subfield of machine learning and recently showed a remarkable performance, especially in classification and segmentation problems. In this paper, a DL model based on a convolutional neural network is proposed to classify different brain tumor types using two publicly available datasets. The former one classifies tumors into (meningioma, glioma, and pituitary tumor). The other one differentiates between the three glioma grades (Grade II, Grade III, and Grade IV). The datasets include 233 and 73 patients with a total of 3064 and 516 images on T1-weighted contrast-enhanced images for the first and second datasets, respectively. The proposed network structure achieves a significant performance with the best overall accuracy of 96.13% and 98.7%, respectively, for the two studies. The results indicate the ability of the model for brain tumor multi-classification purposes.
338 citations
TL;DR: This paper proposes an enhanced approach for classifying brain tumor types using Residual Networks and achieves the highest accuracy of 99% outperforming the other previous work on the same dataset.
186 citations
TL;DR: This study presents a comprehensive review of traditional machine learning techniques and evolving deep learning techniques for brain tumor diagnosis and identifies the key achievements reflected in the performance measurement metrics of the applied algorithms in the three diagnosis processes.
147 citations
TL;DR: The transfer-learning-based AI system is useful in multiclass brain tumour grading and shows better performance than ML systems.
113 citations
13 May 2020
TL;DR: DL design based on a Convolution Neural Network (CNN) to identify various types of brain tumors leveraging two publicly accessible resources or databases is proposed.
Abstract: Identification of brain tumors attends a critical role in evaluating tumors and making decisions about care as per their grades Several imaging methods are employed to identify brain tumors Though, leading to its excellent image quality and the reality that it depends on no cosmic radiation, MRI is widely utilized Deep learning (DL) is a computer vision field of study and has shown remarkable output currently, notably in classification and segmentation issues This article proposes, DL design based on a Convolution Neural Network (CNN) to identify various types of brain tumors leveraging two publicly accessible resources or databases The previous identify tumors into (Meningioma, Glioma, and Pituitary tumors) Another one distinguishes between all three categories (Grade II, Grade III, and Grade IV)
58 citations