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Classification of Breast Thermograms Using Statistical Moments and Entropy Features with Probabilistic Neural Networks
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Author(s): Natarajan Sriraam (M. S. Ramaiah Institute of Technology, India), Leema Murali (M. S. Ramaiah Institute of Technology, India), Amoolya Girish (M. S. Ramaiah Institute of Technology, India), Manjunath Sirur (M. S. Ramaiah Institute of Technology, India), Sushmitha Srinivas (M. S. Ramaiah Institute of Technology, India), Prabha Ravi (M. S. Ramaiah Institute of Technology, India), B. Venkataraman (Indira Gandhi Centre for Atomic Research, India), M. Menaka (Indira Gandhi Centre for Atomic Research, India), A. Shenbagavalli (National Engineering College, India)and Josephine Jeyanathan (Kalasalingam University, India)
Copyright: 2020
Pages: 13
Source title:
Deep Learning and Neural Networks: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-7998-0414-7.ch065
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Abstract
Breast cancer is considered as one of the life-threatening disease among woman population in developing as well as developed countries. This specific study reports on classification of breast thermograms using probabilistic neural network (PNN) with four statistical moments features mean, standard deviation, skewness and kurtosis and two entropy features, Shannon entropy and Wavelet packet entropy. The CLAHE histogram equalization algorithm with uniform and Rayleigh distributions were considered for contrast enhancement of breast thermal images. The asymmetry detection was performed by applying bilateral ratio. A total of 95 test images (normal = 53, abnormal = 42) was considered. Simulation study shows that CLAHE -RD with wavelet entropy features confirms the existence of symmetry on the right and left breast thermal images. An overall classification accuracy of 92.5% was achieved using the proposed multifeatures with PNN classifier. The proposed technique thus confirms the suitability as a screening tool for asymmetry detection as well as classification of breast thermograms.
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