Brain tumor detection and classification using deep learning techniques and MRI imaging

High death rates due to brain tumor draws attention towards the need of accurately detecting brain tumor to improve the treatment outcomes. The objective of this paper is to Classify and detect brain tumors with the help of Image Processing and neural networks. Computer-aided diagnosis (CAD) systems, combined with deep learning, make tumor detection more accurate by analyzing features like location, size, shape, and type. The proposed system integrates CNN for classification including pre-processing, feature extraction, segmentation and classification steps using MRI images and to overcome slow processing a Deep Neural Network (DNN) model with enhanced feature extraction and segmentation phases is proposed, achieving more improved accuracy. When compared to methods like SVM and ANN, the system offers better accuracy, sensitivity, precision, and processing time, achieving an expected accuracy of 99% or higher, setting a benchmark in brain tumor detection.