A Novel Method Based on Convolutional Features with Non-Iterative Learning for Brain Tumor Classification

Brain tumor is a cluster of abnormal and uncontrolled cells growth, leading to a short life expectancy in their highest grade. Accurately and timely clinical diagnosis of such tumors play critical role in treatment planning and patient care. Various image techniques such as Computed Tomography (CT), Ultra-Sound image, Magnetic Resonance Imaging (MRI) and biopsy are used to evaluate brain tumors. Among stated four, MRI is most common used non-invasive technique, however the key challenge with these images is the low-level visual information captured by MRI machines, that needs highlevel interpretation by experienced radiologist. The manual interpretation is a tedious, challenging, and erroneous task. In this paper, we propose a novel Convolutional Feature based Euclidean Distance (ConFED) method for faster and more accurate tumor classification. The method consists of convolutional features and Euclidean distance based one step learning. The proposed method is evaluated on Contrast-Enhanced Magnetic Resonance Images (CE-MRI) benchmark dataset. Proposed method is more generic as it does not use any handcrafted features, requires minimal preprocessing, and can achieve average accuracy of 97.02% using five-fold cross-validation. Extensive experiments, along with statistical tests, revealed that the proposed method has outperformed state-of-the-art classification methods on the CE-MRI dataset.