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Brain tumor classification plays a niche role in medical prognosis and effective treatment process. We have proposed a combined feature and image-based classifier (CFIC) for brain tumor image classification in this study. Carious deep neural network and deep convolutional neural networks (DCNN)-based architectures are proposed for image classification, namely, actual image feature-based classifier (AIFC), segmented image feature-based classifier (SIFC), actual and segmented image feature-based classifier (ASIFC), actual image-based classifier (AIC), segmented image-based classifier (SIC), actual and segmented image-based classifier (ASIC), and finally, CFIC. The Kaggle Brain Tumor Detection 2020 dataset has been used to train and test the proposed classifiers. Among the various classifiers proposed, the CFIC performs better than all other proposed methods. The proposed CFIC method gives significantly better results in terms of sensitivity, specificity, and accuracy with 98.86, 97.14, and 98.97%, respectively, compared with the existing classification methods.

With the agile development of the Internet era, starting from the message transmission to money transactions, everything is online now. Remote user authentication (RUA) is a mechanism in which a remote server verifies the user’s correctness over the shared or public channel. In this paper, we analyze an RUA scheme proposed by Chen for the multi-server environment and prove that their scheme is not secured. We also find numerous vulnerabilities such as password guessing attack, replay attack, Registration Center (RC) spoofing attack, session key verification attack, and perfect forward secrecy attack for Chen’s scheme. After performing the cryptanalysis of Chen’s scheme, we propose a biometric-based RUA scheme for the same multi-server environment. We prove that the proposed authentication scheme achieves higher security than Chen’s scheme with the use of informal security analysis as well as formal security analysis. The formal security analysis of the proposed scheme is done using a widely adopted random oracle method.

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