Comparative Study: Artificial Neural Networks Training Functions for Brain Tumor Segmentation for MRI Images
Brain tumor detection from medical images is essential to diagnose earlier and to take decision in treatment planning. Magnetic Resonance Images (MRI) is frequently preferred for detecting brain tumors by the physicians. This paper analyses various Artificial Neural Networks (ANN) training
functions for brain tumor segmentation such as Levenberg-Marquardt (LM), Quasi Newton back propagation (QN), Bayesian regularization (BR), Resilient back propagation algorithm (RP) and Scaled conjugate gradient back propagation (SCG). The training algorithms were employed in different sized
network for segmentation. The results were carefully analyzed and measured using Dice similarity, sensitivity, specificity and accuracy measures.
Keywords: ANN; Brain Tumor; Feature Extraction; MRI; Training Function
Document Type: Research Article
Affiliations: Department of Computer Science, Madurai Kamaraj University, Madurai 625021, Tamil Nadu, India
Publication date: 01 April 2020
- Journal of Computational and Theoretical Nanoscience is an international peer-reviewed journal with a wide-ranging coverage, consolidates research activities in all aspects of computational and theoretical nanoscience into a single reference source. This journal offers scientists and engineers peer-reviewed research papers in all aspects of computational and theoretical nanoscience and nanotechnology in chemistry, physics, materials science, engineering and biology to publish original full papers and timely state-of-the-art reviews and short communications encompassing the fundamental and applied research.
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