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ABSTRACT
The improvement of technology and Machine Learning can help radiologists in tumor diagnostics without invasive measures. A Machine-learning algorithm that has achieved substantial results in image segmentation and classification is the convolutional neural network (CNN). A new CNN architecture is proposed for brain tumor classification of three stages of tumor types. Initially, the image is filtered using 2D Adaptive Median Filter (2D-AMF) for noise removal. The de-noised image is then enhanced using Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm. From the enhanced image, the Region Of Interest (ROI) is segmented using
clustering and threshold methods. Clustering is performed using Fast Fuzzy C Means Clustering (FFCM) and threshold is performed using Otsu threshold (OT) algorithm. From the ROI, features are extracted using Gray Level Co-Occurrence Matrix (GLCM). GLCM is a feature that computes the occurrence of pixel pairs in specific
spatial coordinates of an image. Finally, the obtained features are classified using machine learning based Convolutional Neural Network (CNN) techniques.
