Journal of Intelligent Systems and Internet of Things JISIoT 2690-6791 2769-786X 10.54216/JISIoT https://www.americaspg.com/journals/show/2526 2019 2019 Department of Electronics and Communication Engineering, K. Ramakrishnan College of Engineering, Tiruchirapalli, India S. S. Department of Computer Science and Engineering, SRMIST Ramapuram Campus, Chennai- 89, India V. Nivedita Department of Information Technology, Panimalar Engineering College, Chennai B. Karthikeyan Department of Computer Science and Engineering, School of Computing, Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology, Chennai, India K. Nithya Department of Medical Equipment Technology, College of Applied Medical Sciences, Majmaah University, Al Majmaah 11952, Saudi Arabia Mohamed Yacin Sikkandar Brain tumors (BT) are abnormal cell growth from the brain or the surrounding cells. It is categorized into 2 major types such as malignant (cancerous) and benign (non-cancerous). Classifying and detecting BTs is critical for knowledge of their mechanisms. Magnetic Resonance Imaging (MRI) is a helpful but time-consuming system, that needs knowledge for manual examination. A new development in Computer-assisted Diagnosis (CAD) and deep learning (DL) allows more reliable BT detection. Typical machine learning (ML) depends on handcrafted features, but DL achieves correct outcomes without such manual extraction. DL methods, particularly convolutional neural networks (CNNs) and recurrent neural networks (RNNs) can exposed to optimum outcomes in the domain of medical image analysis, comprising the classification and recognition of BTs in MRI and CT scans. Thus, the study designs an automated BT Detection and Classification using the Osprey Optimization Algorithm with Deep Learning (BTDC-OOADL) method on MRI Images. The BTDC-OOADL technique deeply investigates the MRI for the identification of BT. In the proposed BTDC-OOADL algorithm, the Wiener filtering (WF) model is applied for the elimination of noise. Besides, the BTDC-OOADL algorithm exploits the MobileNetV2 technique for the procedure of feature extractor. In the meantime, the OOA is utilized for the optimum hyperparameter choice of the MobileNetv2 model. Finally, the graph convolutional network (GCN) model can be deployed for the classification and recognition of BT. The experimental outcome of the BTDC-OOADL methodology can be tested under benchmark dataset. The simulation values infer the betterment of the BTDC-OOADL system with recent approaches. 2024 2024 33 44 10.54216/JISIoT.120103 https://www.americaspg.com/articleinfo/18/show/2526