Abstract Brain tumor is an acute cancerous disease that results from abnormal and uncontrollable cell division. Brain tumors are classified via biopsy, which is not normally done before the brain ultimate surgery. Recent advances and improvements in deep learning (DL) models helped the health industry in getting accurate diseases diagnosis. This article concentrates on the classification of magnetic resonance (MR) images. The objective is to differentiate between glioma tumors, meningioma tumors, pituitary tumors, and normal cases. Four deep convolutional neural networks are considered and compared in this article. These networks are inceptionresnetv2, inceptionv3, transfer learning, and BRAIN‐TUMOR‐net. A transfer‐learning strategy is considered to enhance the performance of pre‐trained models and save the time of training. The used dataset is the brain tumor magnetic resonance imaging dataset. It contains four classes including 826 MR images for glioma tumor, 822 MR images for meningioma tumor, 827 MR images for pituitary tumor, and 835 MR images for normal cases. Due to the limited number of images, we use the augmentation strategy to enlarge the size of the dataset. 75% of the data are considered for training and the other 25% are considered for testing. Segmentation of the classified results is performed. Simulation results prove that the DL model from scratch obtains the highest performance with the augmented data. In addition, a new practical implementation is presented for the proposed models.

Two adaptive algorithms are presented for the detection of small changes in a pair of images in a low signal to clutter plus noise ratio (SCNR) environment. They both have the ability to track the nonstationary image signals and suppress the clutter plus noise background. Both detectors are based on the adaptive correlation canceling technique. One algorithm uses an order recursive least squares (ORLS) lattice filter, while the other is based on the two-dimensional least mean square (TDLMS) algorithm. The only a priori information required by the algorithms is that the background clutter plus noise in the pair of images is spatially correlated. An analytical expression for the improvement factor for the change detectors is presented. The performance of the two algorithms is evaluated by using an optical satellite image, with computer generated target and noise added.>