A Survey Paper on Image Mining Techniques and Classification Brain Tumor

Abstract

Image mining is a method of searching and discovering the valuable information and knowledge from a set of huge image data. Image mining essentially depends on the data mining, digital image processing, machine learning, image retrieval, and artificial intelligence. Image mining is a process which is conducted to extract the hidden information such as image data and the additional pattern that could not be observed from the image. The main problems could face the mining of the collected images can be summarized in two main points: first is the image must be suitable for the mining process and second is the image’s chosen objects and features in order to be treated to extract the most effective route to save the time, and to save the effort. This paper is a survey presents the steps of the image mining process and represented an intensive view on using the image mining to the classify the brain tumors. In addition, it’s proposed a general scheme to accomplish the processes and to analysis the latest techniques which have been used to classify the brain tumors with comparison to the training groups and the amount of accuracy that obtained from the analysis. In addition, the paper compares the relevant and most recent published literature. The high published accuracy claim to be 98% which was obtained using the Deep convolutional neural network (DCNN).