Rajesh Kumar
Allahabad University
6 Papers
14 Citations
Rajesh Kumar is an academic researcher from Allahabad University. The author has contributed to research in topics: Segmentation & Image segmentation. The author has an hindex of 5, co-authored 6 publications. Previous affiliations of Rajesh Kumar include Indian Institutes of Technology & Indian Institute of Technology (BHU) Varanasi.
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Papers
Detection and Classification of Cancer from Microscopic Biopsy Images Using Clinically Significant and Biologically Interpretable Features
Rajesh Kumar,Rajeev Srivastava,Subodh Srivastava +2 more
- 23 Aug 2015
TL;DR: The K-nearest neighborhood method is used for classification of images into normal and cancerous categories because it is performing better in comparison to other commonly used methods for this application.
Histopathological Image Analysis for Breast Cancer Detection Using Cubic SVM
S. K. Singh,Rajesh Kumar +1 more
- 01 Feb 2020
TL;DR: In this paper, a histopathology-based feature has been taken into consideration for breast cancer detection and classification, and the experimental analysis of the proposed approach has been done on publicly available dataset BreakHis.
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Local entropy thresholding based fast retinal vessels segmentation by modifying matched filter
Nagendra Pratap Singh,Rajesh Kumar,Rajeev Srivastava +2 more
- 15 May 2015
TL;DR: An automatic local entropy thresholding based fast, efficient and accurate retinal blood vessels segmentation method by modifying the standard Gaussian shaped matched filter reported in other papers in literature is presented.
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Detection of Cancer from Microscopic Biopsy Images Using Image Processing Tools
Rajesh Kumar,Rajeev Srivastava +1 more
- 01 Jan 2014
TL;DR: An automatic cancer diagnosis system based on microscopic biopsy images using image-processing tools helps pathologists to improve the accuracy and efficiency in detection of malignancy and to minimize the inter observer variation.
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Microscopic Biopsy Image Segmentation Using Hybrid Color K-Means Approach
TL;DR: The experimental analysis shows that the proposed approach is providing better results in terms of accuracy, sensitivity, specificity, FPR false positive rate, global consistency error GCE, probability random index PRI, and variance of information VOI as compared to other segmentation approaches.
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