Classification and Identification of Brain Tumor Using Back Propagation Neural Network with Artificial Bee Colony Optimization Algorithm (Bpnn-Abc)

Abstract

MRI brain images are often corrupted with Intensity In homogeneity artifacts cause unwanted
intensity variation due to non - uniformity in RF coils and noise due to thermal vibrations of
electrons and ions and movement of objects during acquisition which may affect the
performance of image processing techniques used for brain image analysis. In this paper
propose a Back Propagation Neural Network with Artificial Bee Colony Optimization
Algorithm (BPNN-ABC) for classification of brain tumors. The classification of brain tumors
is performed by biopsy, which is not usually conducted before definitive brain surgery. The
improvement of technology and machine learning can help radiologists in tumor diagnostics
without invasive measures. The BPNN-ABC algorithm is finally used for classifying the MRI
of normal, malignant and benign tumor. This method results high accuracy and less iterations
detection which further reduces the consumption time.