A HIGH SPEED BACK PROPAGATION NEURAL NETWORK FOR MULTISTAGE MR BRAIN TUMOR IMAGE SEGMENTATION

作者：Hemanth D Jude; Vijila C Kezi Selva; Anitha J^*

来源：Neural Network World, 2011, 21(1): 51-66.

• Back propagation
• neural network
• MR brain image
• high speed BPN
• convergence time period

Artificial neural networks (ANN) are one of the highly preferred artificial intelligence techniques for brain image segmentation. The commonly used ANN is the supervised ANN, namely Back Propagation Neural Network (BPN). Even though BPNs guarantee high efficiency, they are computationally non-feasible due to the huge convergence time period. In this work, the aspect of computational complexity is tackled using the proposed high speed BPN algorithm (HSBPN). In this modified approach, the weight vectors are calculated without any training methodology. Magnetic resonance (MR) brain tumor images of three stages; namely severe, moderate and mild, are used in this work. An extensive feature set is extracted from these images and used as input for the neural network. A comparative analysis is performed between the conventional BPN and the HSBPN in terms of convergence time period and segmentation efficiency. Experimental results show the superior nature of HSBPN in terms of the performance measures.

• 出版日期2011