MRI segmentation is a crucial task in many clinical applications. A variety of approaches for brain analysis rely on accurate segmentation of anatomical regions. Quantitative analysis of brain MRI has been used extensively for the characterization of brain disorders such as Alzheimer’s, epilepsy, schizophrenia, multiple sclerosis, cancer, and many infectious, degenerative diseases. Manual Segmentation requires outlining structures slice-by-slice, it is not only expensive and tedious but also inaccurate due to human error. Also, manual segmentation is extremely time-consuming and initial hours of brain tumor and strokes are crucial to diagnose it. Therefore, automated segmentation procedures are needed to ensure accuracy close to that of experts with high consistency. We propose to create a Deep Learning based Brain Segmentation solution that would fully automate the process of Brain Tumor Segmentation to solve those cases which are generally missed by the human eye and save time.
Steganography is one expanding filed in the area of Data Security. Steganography has attractive number of application from a vast number of researchers. The most existing technique in steganogarphy is Least Significant Bit (LSB) encoding. Now a day there has been so many new approaches employing with different techniques like deep learning. Those techniques are used to address the problems of steganography. Now a day’s many of the exisiting algorithms are based on the image to data, image to image steganography. In this paper we hide secret audio into the digital image with the help of deep learning techniques. We use a joint deep neural network concept it consist of two sub models. The first model is responsible for hiding digital audio into a digital image. The second model is responsible for returning a digital audio from the stego image. Various vast experiments are conducted with a set of 24K images and also for various sizes of images. From the experiments it can be seen proposed method is performing more effective than the existing methods. The proposed method also concentrates the integrity of the digital image and audio files.
Data Compression in Cryptography is one of the interesting research topic. The compression process reduces the amount of transferring data as well as storage space which in turn effects the usage of bandwidth. Further, when a plain text is converted to cipher text, the length of the cipher text becomes large. This adds up to tremendous information storing. It is extremely important to address the storage capacity issue along with the security issues of exponentially developing information. This problem can be resolved by compressing the ciphertext based on a some compression algorithm. In this proposed work used the compression technique called palindrome compression technique. The compression ratio of the proposed method is better than the standard method for both colored and gray scaled images. An experimental result for the proposed methods is better than existing methods for different types of image.
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