A Novel Technique for Brain Tumor Detection and Classification Using T1-Weighted MR Image

Brain tumors are particularly perilous because they form when cells in the brain multiply uncontrollably within the skull. Therefore, a fast and accurate method of diagnosing tumors is crucial for the patient's health. This study proposes a method for evaluating brain cancer images. The phases of implementation for the proposed work are as follows: In the first phase, we compiled a set of specialized feature vector descriptions for advanced classification tasks by employing both deep learning (DL) and conventional feature extraction techniques. In the second phase, we employ a proposed convolutional neural network (CNN) approach and a traditional subset of features from a genetic algorithm (GA) to select our deep features. The third phase involves using the fusion method to merge the prioritized features. Finally, deter-mine whether the brain image is normal or abnormal. The results showed that the proposed method successfully classified objects accurately and revealed their robustness across differ- ent ages and acquisition protocols. According to the results, the classification accuracy of the support vector machines (SVM) classifier has significantly improved by combining conven- tional features and deep learning features (DLF), achieving an accuracy of up to 86.50% using the T1 weighted brain MR image.