Brain tumor identification with a hybrid feature extraction method based on discrete wavelet transform and principle component analysis

Abstract

The Identification of brain tumors is a critical step that relies on the expertise and abilities of the physician. In order to enable radiologists to spot brain tumors, an automated tumor arrangement is extremely important. This paper presents a technique for MR brain image segmentation and classification to identify images as normal and abnormal. The proposed technique is a hybrid feature extraction submitted to enhance the classification results and basically consists of three stages. The first stage is used a 3-level of discrete wavelet transform (DWT) to extract image characteristics. In the second stage, the principle component analysis (PCA) is applied to reduce the size of characteristics. Finally, a random forest classifier (RF) was used with a feature selection for identification. 181 MR brain images are collected (81 normal and 100 abnormal), in distinguishing normal and abnormal tissues, the experimental results obtained an accuracy of 98%, the sensitivity achieved is 99.2%, specificity achieved is 97.8%, and showed the effectiveness of the proposed technique compared with many kinds of literature. The results show that the 3L-DWT+PCA+RF still achieved the best classification results. The proposed model could apply to the brain MRI sphere classification, which will help doctors to diagnose a tumor if it is normal or abnormal in certain degrees.