The science of solving clinical problems by analyzing images generated in clinical practice is known as medical image analysis. The aim is to extract information in an affective and efficient manner for improved clinical diagnosis. The recent advances in the field of biomedical engineering has made medical image analysis one of the top research and development area. One of the reason for this advancement is the application of machine learning techniques for the analysis of medical images. Deep learning is successfully used as a tool for machine learning, where a neural network is capable of automatically learning features. This is in contrast to those methods where traditionally hand crafted features are used. The selection and calculation of these features is a challenging task. Among deep learning techniques, deep convolutional networks are actively used for the purpose of medical image analysis. This include application areas such as segmentation, abnormality detection, disease classification, computer aided diagnosis and retrieval.

User authentication in wireless sensor networks (WSN) is a critical security issue due to their unattended and hostile deployment in the field. Since sensor nodes are equipped with limited computing power, storage, and communication modules; authenticating remote users in such resource-constrained environments is a paramount security concern. Recently, M.L. Das proposed a two-factor user authentication scheme in WSNs and claimed that his scheme is secure against different kinds of attack. However, in this paper, we show that the M.L. Das-scheme has some critical security pitfalls and cannot be recommended for real applications. We point out that in his scheme: users cannot change/update their passwords, it does not provide mutual authentication between gateway node and sensor node, and is vulnerable to gateway node bypassing attack and privileged-insider attack. To overcome the inherent security weaknesses of the M.L. Das-scheme, we propose improvements and security patches that attempt to fix the susceptibilities of his scheme. The proposed security improvements can be incorporated in the M.L. Das-scheme for achieving a more secure and robust two-factor user authentication in WSNs.

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