Tumors Classification using PNN Methods
M. Vinay Kumar1, Sharad Kulkarini2
1M.Vinay Kumar, Electronics & Communication Engineering, Sri Kottam Tulasi Reddy College Of Engineering & Technology, India.
2Sharad Kulkarini, Electronics & Communication Engineering, Sri Kottam Tulasi Reddy College Of Engineering & Technology, India.
Manuscript received on November 01, 2012. | Revised Manuscript received on November 02, 2012. | Manuscript published on November 05, 2012. | PP: 266-268 | Volume-2 Issue-5, November 2012. | Retrieval Number: E1061102512/2012©BEIESP
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In this paper, pnn with image and data processing techniques was employed to implement an automated brain tumor classification. The conventional method for medical resonance brain images classification and tumors detection is by human inspection. Operator-assisted classification methods are impractical for large amounts of data and are also non-reproducible. Medical Resonance images contain a noise caused by Operator performance which can lead to serious inaccuracies classification. The use of artificial intelligent techniques for instant, neural networks, and fuzzy logic shown great potential in this field. Hence, in this paper the Probabilistic Neural Network was applied for the purposes. Decision making was performed in two stages: feature extraction using the principal component analysis and the Probabilistic Neural Network (PNN). The performance of the PNN classifier was evaluated in terms of training performance and classification accuracies. Probabilistic Neural Network gives fast and accurate classification and is a promising tool for classification of the tumors.
Keywords: PNN, Neural Network, PCA, MRI, Classification