Journal of Cybersecurity and Information Management JCIM 2690-6775 2769-7851 10.54216/JCIM https://www.americaspg.com/journals/show/2962 2019 2019 Department of Computer Science andEngineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, India Saran Saran Professor, Department of Artificial Intelligence and Data Science, KPR Institute of Engineering and Technology, Coimbatore, India S. V. Sudha Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaran, Guntur, Andhra Pradesh, India K. Padmanaban Assistant Professor, Department of Information Technology, Karpagam College of Engineering, Coimbatore, India P. Sherubha Associate Professor, Department of Computer science and Engineering, Karpagam Academy of Higher Education, Coimbatore, India S. P. Sasirekha Brain tumor is a condition due to the expansion of abnormal cell growth. Tumors are rare and can take many forms; it is challenging to estimate the survival rate of a patient. These tumors are found using Magnetic Resonance (MRI) which is crucial for locating the tumor region. Moreover, manual identification is an extensive and difficult method to produce false positives. The research communities have adopted computer-aided methods to overcome these limitations. With the advancement of artificial intelligence (AI), brain tumor prediction relies on MR images and deep learning (DL) models in medical imaging. The suggested layered configurations, i.e., layered network model, are proposed to classify and detect brain tumors accurately. The modified CNN is proposed to automatically detect the important features without any supervision and the convolution layer present in the network model enhances the training feasibility. To improve the quality of the images, some essential pre-processing is used in conjunction with image-enhancing methods. Data augmentation is adopted to expand the number of data samples for our suggested model's training.  The Dataset is portioned as based on 70% for training and 30% for testing. The findings demonstrate that the proposed model works well than existing models in classification precision, accuracy, recall, and area under the curve. The layered network model beats other CNN models and achieves an overall accuracy of 99% during prediction. In addition, VGG16, hybrid CNN and NADE, CNN, CNN and KELM, deep CNN with data augmentation, CNN-GA, hybrid VGG16-NADE and ResNet+SE approaches are used for comparison.   2024 2024 18 32 10.54216/JCIM.140202 https://www.americaspg.com/articleinfo/2/show/2962