Proceedings ArticleDOI
Detection & Classification of Tumor Cells from Bone MR Imagery Using Connected Component Analysis & Neural Network
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TLDR
In this work, the artificial neural network (ANN) is used for the classification of bone tumor and the obtained performance result exhibit that the neural network provides 92.50% success rate in bone tumor classification.Abstract:
Bone cancer is a class of diseases that are characterized by an unfettered growth of the cell and it is considered to be the main reasons of early death around the globe. Therefore, early detection and classification of the bone tumor are become needed to cure the patient. This study uses a connected component labeling algorithm for the detection of the bone tumor. In this work, the artificial neural network (ANN) is used for the classification of bone tumor. Total 220 bone MR images of previously verified patients are collected and the texture features of this images are used for the training and testing of the neural network. The obtained performance of the classification result exhibit that the neural network provides 92.50% success rate in bone tumor classification.read more
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References
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Book
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Vincent T. DeVita,Vincent T. DeVita,Theodore S. Lawrence,Steven A. Rosenberg,Steven A. Rosenberg +4 more
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Proceedings ArticleDOI
Bone Cancer Detection from MRI Scan Imagery Using Mean Pixel Intensity
TL;DR: This paper used k means clustering algorithm for bone image segmentation, which gives 95% accuracy with less computational time for bone cancer detection and classification.
Proceedings ArticleDOI
Brain tumor types and grades classification based on statistical feature set using support vector machine
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