Segmenting the Lesion Area of Brain Tumor using Convolutional Neural Networks and Fuzzy K-Means Clustering

Brain tumor Segmentation is one of the most crucial methods of medical image processing. Non-automatic segmentations are broadly used in clinical diagnosis and medication. However, this kind of segmentation does not have accuracy in medical images, especially in terms of brain tumors, and it provides a low level of reliability. The primary objective of this paper is to develop a methodology for brain tumor segmentation. In this paper, a combination of Convolutional Neural Network and Fuzzy K-means algorithm has been presented to segment the lesion area of brain tumor. It contains three phases, Image preprocessing to reduce computational complexity, Attribute extraction and selection and Segmentation. At first, the database images are pre-processed using adaptive filters and wavelet transform in order to recover the image from the noise state and reduce the computational complexity. Then feature extraction is performed by the proposed deep neural network. Finally, it is processed through the Fuzzy K-Means algorithm to segment the tumor region separately. The innovation of this article is related to the implementation of deep neural network with optimal parameters, identification of related features and removal of unrelated and repetitive features with the aim of observing a subset of features that describe the problem well and with minimal reduction in efficiency. This results in reduced feature sets, storage of data collection resources during operation, and overall data reduction to limit storage requirements. This proposed segmentation approach has been verified on BRATS dataset and produces the accuracy of 98.64%, sensitivity of 100% specificity of 99%.