ICM-BTD: improved classification model for brain tumor diagnosis using discrete wavelet transform-based feature extraction and SVM classifier

In medical image processing, the detection, classification and segmentation of the tumor region from MRI scans accurately are very complicated, significant and time-consuming process. When there is a scenario occurs to handle with large amount of images for tumor diagnosis, there is need of an efficient and adaptive classification model to handle with the anomalous structures of human brains. The MRI brain images show the typical internal brain structure and hence help scholars and medical practitioners in accurate disease diagnosis. With that note, this paper develops a model called improved classification model for brain tumor diagnosis for appropriate classification of tumor images from input MRI images. Initially, filtering techniques are applied for preprocessing the acquired scan images and feature extraction is done with gray-level co-occurrence matrix and discrete wavelet transform equations, which produces more precise results. And, classification is done with the technique called support vector machine, in which the binary classifications are effectively done. The proposed model is evaluated under simulation, and the obtained results outperform the results of traditional brain tumor detection process based on precision, recall and processing time.