Brain is an organ that controls activities of all the parts of the body. Recognition of automated brain tumor in Magnetic resonance imaging (MRI) is a difficult task due to complexity of size and location variability. This automatic method detects all the type of cancer present in the body. Previous methods for tumor are time consuming and less accurate. In the present work, statistical analysis morphological and thresholding techniques are used to process the images obtained by MRI. Feed-forward backprop neural network is used to classify the performance of tumors part of the image. This method results high accuracy and less iterations detection which further reduces the consumption time.

Now days due to advancement of technology it is difficult to protect creative content and intellectual property. It is very easy to copy and modify digital media resulting in great loss in business. So the viable solution for this problem is digital watermarking. Digital watermarking is a technique by which we embed copyright mark into digital content which is used to identify the original creator and owner of digital media. It is prominently used for tracing copyright infringements. In this paper technique based on 1-level discrete wavelet transform is used for insertion and extraction of watermark in original image by using alpha blending. This technique is much simpler and robust than others.

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Brain tumor segmentation is necessitated to ascertain the severity of tumor growth in a brain for possible treatment planning. In this work, we attempt the development of U‐Net‐based semantic segmentation of brain tumors. This network model is trained and tested on three MRI datasets: Brats 2018, Brats 2019, and Brats 2020. The trained U‐Net yields the dice scores of 0.893, 0.837, and 0.753 on Brats 2018; 0.912, 0.891, and 0.808 on Brats 2019, and of 0.917, 0.894, and 0.811 on Brats 2020 for the complete tumor, tumor core, and enhancing tumor respectively. This paper also presents a novel formulation of a regression model based on an Information set to predict the survival rates of patients affected with a brain tumor. The weights of the regression model are learned using the pervasive learning model based on the pervasive information set. The overall survival rates of patients are predicted using the proposed regression model on the three datasets for which High‐Grade Gliomas subjects are considered, and the model achieves the accuracies of 64.2%, 59.8%, and 60.5% on Brats 2018, 2019, and 2020 datasets respectively.

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