An Efficient Noise Reduction Technique and Classification of Osteosarcoma by the Machine Learning Approach

Cancer is the one of the most dangerous and uncontrollable diseases in this world. It spreads rapidly in living organisms. To identify the disease early from various parts of the body medical image processing plays a major role in the medical field. Among the various diseases; an osteosarcoma is the most dangerous disease and leads to death in human life. To classify the sarcoma with early symptoms and accurate analysis with less noise in prediction has a major role in the medical field. To reduce such complexity, machine learning techniques has used. It contains large number of data set with various morphological and operational techniques. To classify the images primary methods such as preprocessing, edge detection are used. In continue with primary methods segmentation along with feature extraction are used to classification of images with the help of support vector machine. It has a special feature to classify all the images exactly based on input data. With the help of training and testing data in SVM classifier classify the cancerous and non-cancerous image with efficient accuracy with reduced time.