Brain Tumor Categorization and Retrieval Using Deep Brain Incep Res Architecture Based Reinforcement Learning Network

Abstract

The categorization and retrieval of brain tumors using Magnetic Resonance Imaging (MRI) is a difficult but necessary process for brain tumor diagnosis. In this study, a reinforcement learning agent is proposed that can interact with an environment that includes brain tumor images and retrieve and categorize the most comparable images to an unknown query image. This article proposes a unique fuzzy and Deep Learning (DL)-based Reinforcement Learning (RL) strategy for categorizing three types of brain tumors as well as no tumors. Deep Brain Incep Res Architecture 2.0 based Reinforcement Learning Network (DBIRA2.0-RLN), the proposed Convolutional Neural Network (CNN)-based technique, benefits from a novel architecture in which brain tumor descriptors are established using the inception block and effective skip-connection mapping arrangement. To improve the efficiency of DBIRA2.0-RLN, improved samples are created by training and testing the system with a fuzzy logic-based technique. To lower the dimension of the descriptor vector for improved image categorization and retrieval, the descriptor vector obtained from DBIRA2.0 is binary coded using Multilinear Principal Component Analysis. DBIRA2.0 produces and preserves brain tumors and no tumor descriptors in several layers, which are then used sequentially in numerous units to construct the final brain tumor categorization and retrieval. The proposed method’s output is tested using a dataset, and the accuracy rates obtained for meningioma tumor, glioma tumor, pituitary tumor, and no tumor are 97.1%, 98.7%, 94.3%, and 100% respectively, indicating that the proposed approach outperforms the other brain tumor categorization and retrieval approaches used in the literature.

Keywords

• Brain tumor image
• deep neural network
• fuzzy inference system
• inception block
• residual network
• reinforcement learning