Vol.:(0123456789) https://doi.org/10.1007/s11042-021-11504-9 1 3 1171: REAL-TIME 2D/ 3D IMAGE PROCESSING WITH DEEP LEARNING 2D MRI image analysis and brain tumor detection using deep learning CNN model LeU‑Net Hari Mohan Rai 1  · Kalyan Chatterjee 1 Received: 29 June 2020 / Revised: 3 July 2021 / Accepted: 19 August 2021 © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract MRI image analysis and its segmentation for the accurate and automatic detection of brain tumors at an early stage is very much crucial for diagnosis the disorders and save human lives. Since most deep learning models have a large number of layers, they also take longer processing time, making them unsuitable for smaller image datasets. Hence, we have pro- posed, the detection of abnormality from brain MR images using a Less Layered and less complex U-Net model (LeU-Net) architecture. The principle of LeU-Net is inspired by the Le-Net and U-Net models, but completely diferent from both the design and architec- tural perspectives. The abnormality detection indicates the classifcation of the tumorous cell from overall Magnetic Resonance images. The Proposed deep learning model (LeU- Net) performance was compared with the existing basic CNN models Le-Net, U-Net, and VGG-16. The model performance was evaluated using evaluation metrics accuracy, preci- sion, F-score, recall, and specifcity. The experiment is performed on MR Dataset with uncropped images and cropped images (removed unwanted area) and compared the result with all three models. The LeU-Net model registers overall 98% accuracy on cropped images and 94% of accuracy on uncropped images. The LeU-Net model has much faster processing (simulation) time, it only takes 244.42 s and 252.36 s, respectively, to train the model with 100 epochs on the uncropped and cropped images. We have compared the per- formance of our proposed model with various state-of-the-art techniques, and it provides the best classifcation accuracy among all. Keywords LeU-Net · Biomedical image analysis · Deep neural network · Convolutional neural network · Magnetic resonance imaging · Artifcial neural network * Hari Mohan Rai harimohanrai@gmail.com Kalyan Chatterjee kalyanchatterjee@iitism.ac.in 1 Department of Electrical Engineering, Indian Institute of Technology (ISM) Dhanbad, Dhanbad, India Published online: 30 October 2021 Multimedia Tools and Applications (2021) 80:36111–36141 /