Author(s): K. Srilatha, V. Ulagamuthalvi

Email(s): srilatha169@gmail.com

DOI: 10.5958/0974-360X.2019.00074.X   

Address: K. Srilatha1, V. Ulagamuthalvi2
1Assistant Professor, Department of Electronics and Communication Engineering, Sathyabama Institute of Science and Technology, Chennai, India.
2Assistant Professor, Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Chennai, India.
*Corresponding Author

Published In:   Volume - 12,      Issue - 1,     Year - 2019


ABSTRACT:
Cancer detection is the most significant method to identify the early tumor. Enlargement of the tumor is being a huge task due to the complex characteristics of the medical images which provides high divergent, intensive and uncertain boundaries. Designing and developing computer-aided image processing systems are to help doctors improve their diagnosis and then received huge benefits over the past years. Classification is an important task within the field of computer vision. Image classification refers to the labelling of images into one of a number of predefined categories that includes image sensors, image pre-processing, object detection, object segmentation, feature extraction and object classification. Many classification techniques have been developed for image classification. The aim of literature survey is to provide a brief summary about some of common most image classification technique and comparison among them. In this survey various classification techniques are considered; Artificial Neural Network (ANN), Decision Tree (DT), Support Vector Machine (SVM), Fuzzy Classification and more.


Cite this article:
K. Srilatha, V. Ulagamuthalvi. A Comparative Study on Tumour Classification. Research J. Pharm. and Tech 2019; 12(1): 407-411. doi: 10.5958/0974-360X.2019.00074.X

Cite(Electronic):
K. Srilatha, V. Ulagamuthalvi. A Comparative Study on Tumour Classification. Research J. Pharm. and Tech 2019; 12(1): 407-411. doi: 10.5958/0974-360X.2019.00074.X   Available on: https://rjptonline.org/AbstractView.aspx?PID=2019-12-1-74


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RNI: CHHENG00387/33/1/2008-TC                     
DOI: 10.5958/0974-360X 

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