Reversible data hiding in encrypted images (RDH-EI) has got some promising applications in cloud storage, medical imaging, forensics etc. It has gained considerable research interest in recent years. In this paper, we follow the paradigm of reserving room for additional data before encrypting the image and propose a new method by classifying the smooth and coarse blocks of the image. LSB-planes of coarse blocks are reserved to hide additional data. Smooth blocks are used to back-up the locations reserved for those additional data, using a traditional RDH technique that works on unencrypted images. Experiments show that proposed method gives superior performance than the state-of-the-art methods and is suitable for applications that demand high data hiding capacity.

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Cancer grade is an indicator of the aggressiveness of cancer. It is used for prognosis and treatment decisions. Conventionally cancer grading is performed manually by experienced pathologists via microscopic examination of pathology slides. Among the three factors involved in breast cancer grading (mitosis count, nuclear atypia, and tubule formation), mitotic cell counting is the most challenging task for pathologists. It is possible to automate this task by applying computational algorithms on pathology slides images. Lack of sufficiently large datasets and class imbalance between mitotic and non-mitotic cells in slide images are the two major challenges in developing effective deep learning-based methods for mitosis detection. In this paper, we propose a new approach and a method based on that to address these challenges. The high training data requirement of the advanced deep neural network is met by combining two datasets from different sources after a color-normalization process. Class imbalance is addressed by the augmentation of the mitotic samples in a