Mesothelioma is a form of cancer that is aggressive and fatal. It is a thin layer of tissue that covers the majority of the patient’s internal organs. The treatments are available; however, a cure is not attainable for the majority of patients. So, a lot of research is being done on detection of mesothelioma cancer using various different approaches; but this paper focuses on optimization techniques for optimizing the biomedical images to detect the cancer. With the restricted number of samples in the medical field, a Relief-PSO head and mesothelioma neck cancer pathological image feature selection approach is proposed. The approach reduces multilevel dimensionality. To begin, the relief technique picks different feature weights depending on the relationship between features and categories. Second, the hybrid binary particle swarm optimization (HBPSO) is suggested to automatically determine the optimum feature subset for candidate feature subsets. The technique outperforms seven other feature selection algorithms in terms of morphological feature screening, dimensionality reduction, and classification performance.
Prostate cancer is one of the most common cancers in men worldwide, second only to lung cancer. The most common method used in diagnosing prostate cancer is the microscopic observation of stained biopsies by a pathologist and the Gleason score of the tissue microarray images. However, scoring prostate cancer tissue microarrays by pathologists using Gleason mode under many tissue microarray images is time-consuming, susceptible to subjective factors between different observers, and has low reproducibility. We have used the two most common technologies, deep learning, and computer vision, in this research, as the development of deep learning and computer vision has made pathology computer-aided diagnosis systems more objective and repeatable. Furthermore, the U-Net network, which is used in our study, is the most extensively used network in medical image segmentation. Unlike the classifiers used in previous studies, a region segmentation model based on an improved U-Net network is proposed in our research, which fuses deep and shallow layers through densely connected blocks. At the same time, the features of each scale are supervised. As an outcome of the research, the network parameters can be reduced, the computational efficiency can be improved, and the method’s effectiveness is verified on a fully annotated dataset.
The ever increasing technologies with parallel advancements in the development of notorious attempts, to play with the integrity of the information, in the field of communication over the internet present the need for the equally enhancing security measures. We here, try to analyze the security steps and related phenomena which have been developed specific to the symmetric cryptosystems. With the application of different protocols and their related drawbacks, we try to introduce a few changes, if possible, without diminishing the essence of such protocols. Our attempt is to look for the possibility of introducing asymmetric cryptosystem security measures to meet our purpose. The works already being done are a source of reference to invoke something inquisitive towards our topic. Such works include techniques such as key management, magic rectangle, hash functions, encrypted key exchange, etc.
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