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In the medical sector, the digital image is multimedia data that contain secret information. However, designing an efficient secure cryptosystem to protect the confidential images in sharing is a challenge. In this work, we propose an improved chaos-based cryptosystem to encrypt and decrypt rapidly secret medical images. A complex chaos-based PRNG is suggested to generate a high-quality key that presents high randomness behaviour, high entropy, and high complexity. An improved architecture is proposed to encrypt the secret image that is based on permutation, substitution, and diffusion properties. In the first step, the image’s pixels are randomly permuted through a matrix generated using the PRNG. Next, pixel’s bits are permuted using an internal condition. After that, the pixels are substituted using two different S-boxes with an internal condition. In the final step, the image is diffused by XORing pixels with the key stream generated by the PRNG in order to acquire an encrypted image. R rounds of encryption can be performed in a loop to increase the complexity. The cryptosystem is evaluated in depth by his application on several medical images with different types, contents, and sizes. The obtained simulation results demonstrate that the system enables high-level security and performance. The information entropy of the encrypted image has achieved an average of 7.9998 which is the most important feature of randomness. The algorithm can take full advantage of parallelism and pipeline execution in hardware implementation to meet real-time requirements. The PRNG was tested by NIST 800-22 test suit, which indicates that it is suitable for secure image encryption. It provides a large key space of 2192 which resists the brute-force attack. However, the cryptosystem is appropriate for medical image securing.

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The automatic diagnosis of breast cancer (BC) is an important, real-world medical problem. This paper proposes a design of automated detection, segmentation, and classification of breast cancer nuclei using a fuzzy logic. The first step is based on segmentation using an active contour for cell tracking and isolating of the nucleus in the cytological image. Then from this nucleus, have been extracted some textural features using the wavelet transforms to characterize image using its texture, so that malign texture can be differentiated from benign one with the assumption that tumoral texture is different from the texture of other kinds of tissues. Finally, the obtained features will be introduced as the input vector of a fuzzy C-means (FCM) clustering algorithm to classify the images into malign and benign ones. The implementation of such algorithm has been done using a methodology based on very high speed integrated circuit, hardware description language (VHDL). The design of the circuit is performed by using a CMOS 0.35 μm technology.

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Deep network in network (DNIN) model is an efficient instance and an important extension of the convolutional neural network (CNN) consisting of alternating convolutional layers and pooling layers. In this model, a multilayer perceptron (MLP), a nonlinear function, is exploited to replace the linear filter for convolution. Increasing the depth of DNIN can also help improve classification accuracy while its formation becomes more difficult, learning time gets slower, and accuracy becomes saturated and then degrades. This paper presents a new deep residual network in network (DrNIN) model that represents a deeper model of DNIN. This model represents an interesting architecture for on-chip implementations on FPGAs. In fact, it can be applied to a variety of image recognition applications. This model has a homogeneous and multilength architecture with the hyperparameter “L” (“L” defines the model length). In this paper, we will apply the residual learning framework to DNIN and we will explicitly reformulate convolutional layers as residual learning functions to solve the vanishing gradient problem and facilitate and speed up the learning process. We will provide a comprehensive study showing that DrNIN models can gain accuracy from a significantly increased depth. On the CIFAR-10 dataset, we evaluate the proposed models with a depth of up to L = 5 DrMLPconv layers, 1.66x deeper than DNIN. The experimental results demonstrate the efficiency of the proposed method and its role in providing the model with a greater capacity to represent features and thus leading to better recognition performance.

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In this paper, we propose an efficient cryptosystem for digital image encryption and authentication. The cryptosystem is a hybrid scheme that uses symmetric and asymmetric approaches. The first one is used to encrypt the host image by utilizing a chaos-based key generator. The second one is used to encrypt the initial secret key and the owner’s signature that permit authentication. The algorithm is evaluated and validated by its application on several types of standard images and tools such as the statistical analysis, the key, and the performance analysis. The results indicate that the proposed cryptosystem provides high performance and enhanced security. The NIST 800-22 is used for testing the pseudorandom numbers generation (PRNG). The obtained simulation results are better than those cited in the recent works in terms of execution time and security level and low computational complexity.