Bone Cancer Detection Classification Using Fuzzy Clustering Neuro Fuzzy Classifier

Abstract

Bone cancer is one of the most dangerous and main reasons for early death around the globe. Therefore, early detection and classification of the bone cancer have become needed to cure the patient. This study approaches a method for the detection of bone cancer using fuzzy C-mean clustering. Total 120 verified patient magnetic resonance images (MRI) of bones has been used for the accuracy checking of the proposed method. This study uses adaptive neuro fuzzy inference system (ANFIS) for the classification of benign and malignant bone cancer. Gray level co-occurrence matrix (GLCM) features have been taken from the MR images for the training and testing of the ANFIS network. A proper cross validation has been done over the collected bone images to separate them into training and testing images. The classification result has been evaluated based on three performance matrices accuracy, sensitivity and specificity. The proposed classification technique provides 93.75% accuracy in bone cancer classification.