The Brain Tumors is the one of the leading disease affects the humans, thus the early detection of brain tumors prevent millions of deaths. Thus, most of the researches are focusing on detection of brain tumor using machine learning based approaches. But, those approaches are failed to provide the classification accuracy. To overcome these drawbacks, in this work Adative Neuron Fuzzy Inference System (ANFIS) based Deep Learning based Convolution Neural Networks (DLCNN) classification algorithm has been performing with the help of effective use of Grey level Co-occurrence Matrix (GLCM) features. Initially, Probabilistic Kernel Fuzzy C Means Segmentation (PKFCM) based multi level segmentation operation has been performed to detection of accurate tumor region. The simulations are conducted on various datasets, the results shows that the proposed work shows the better performance compared to various conventional approaches with respect to both quantitative and qualitative evaluation.

Keywords : Brain Tumor , Detection, Disease, Fuzzy, Machine Learning , Deep Learning , Convolution and Segmentation.