The five-year survival rate for pancreatic cancer (PC) is the lowest of any cancer kind, and it is the fourth greatest cause of cancer-related death, with a growing death rate. When it comes to cancer invasion, the most significant risk factors are: smoking; alcohol usage; diabetes; and prior pancreatitis. By using this method, we will be able to detect our PC, which is equipped with picture handling technology. Researchers used CT images as input in this study and preprocessed them to remove any noise in the images that had been learned using an adaptive Weiner filter. Preprocessing is followed by the use of a region grow ideal to segment the noise-free image. Scale Invariant Feature Transform (SIFT) is utilized once more to extract the tumor limits and principal component analysis (PCA) is used to enhance the retrieved structures to improve the types of pancreatic CT images. In order to activate the picture parameters, a convolutional neural network (CNN) classifier is used. In order to categorize an image as nonpancreatic cancer or pancreatic cancer, the test data were compared to the training data and the classified image was compared. MATLAB then initiates the entire process, and the most recent performance estimation approach is utilized, resulting in outstanding accuracy.
Pancreatic tumor is the deadliest disease which needs earlier identification to reduce the mortality rate. With this motivation, this study introduces a Multi-Objective Metaheuristics with Intelligent Deep Learning Model for Pancreatic Tumor Diagnosis (MOM-IDL) model. The proposed MOM-IDL technique encompasses an adaptive Weiner filter based pre-processing technique to enhance the image quality and get rid of the noise. In addition, multi-level thresholding based segmentation using Kapur’s entropy is employed where the threshold values are optimally chosen by the barnacles mating optimizer (BMO). Besides, densely connected network (DenseNet-169) is employed as a feature extractor and fuzzy support vector machine (FSVM) is utilized as a classifier. For improving the classification performance, the BMO technique was implemented for fine-tuning the parameters of the FSVM model. The design of MOBMO algorithm for threshold selection and parameter optimization processes shows the novelty of the work. A wide range of simulations take place on the benchmark dataset and the experimental results highlighted the enhanced performance of the MOM-IDL technique over the recent state of art techniques.
IN modern years Steganography is playing a significant role in secure communication. It is a technique of embedding secret information into cover media (image, video, audio and text) such that only the sender and the authoritative receiver can detect the occurrence of hidden information. The two essential properties of Steganography are good visual imperceptibility of the payload which is crucial for security of hidden communication and payload is essential for conveying huge quantity of secret information. Steganography has to satisfy two requirements, one is capability and the other is transparency. Capability means embedding large payload into media. Transparency means an ability to prevent distinction between stego and cover image by statistical analysis. Earlier they have used least significant bit (LSB), the simplest form of Steganography. In LSB method, data is inserted in the least significant bit which leads to a negligible change on the cover image that is not visible to the naked eye. Since this method can be easily cracked, it is more exposed to attacks. In the proposed system we propose Spatial Domain Steganography using 1-Bit Most Significant Bit (MSB) with confused manner.
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