Hybrid Models for Breast Cancer Detection via Transfer Learning Technique

Currently, breast cancer has been a major cause of deaths in women worldwide and the World Health Organization (WHO) has confirmed this. The severity of this disease can be minimized to the large extend, if it is diagnosed properly at an early stage of the disease. Therefore, the proper treatment of a patient having cancer can be processed in better way, if it can be diagnosed properly as early as possible using the better algorithms. Moreover, it has been currently observed that the deep neural networks have delivered remarkable performance for detecting cancer in histopathological images of breast tissues. To address the above said issues, this paper presents a hybrid model using the transfer learning to study the histopathological images, which help in detection and rectification of the disease at a low cost. Extensive dataset experiments were carried out to validate the suggested hybrid model in this paper. The experimental results show that the proposed model outperformed the baseline methods, with F-scores of 0.81 for DenseNet + Logistic Regression hybrid model, (F-score: 0.73) for Visual Geometry Group (VGG) + Logistic Regression hybrid model, (F-score: 0.74) for VGG + Random Forest, (F-score: 0.79) for DenseNet + Random Forest, and (F-score: 0.79) for VGG + Densenet + Logistic Regression hybrid model on the dataset of histopathological images.     

Double Encryption Using Trigonometric Chaotic Map and XOR of an Image

In the most recent decades, a major number of image encryption plans have been proposed. The vast majority of these plans reached a high-security level; however, their moderate speeds because of their complicated processes made them of no use in real-time applications. Inspired by this, we propose another efficient and rapid image encryption plan dependent on the Trigonometric chaotic guide. In contrast to the most of current plans, we utilize this basic map to create just a couple of arbitrary rows and columns. Moreover, to additionally speed up, we raise the processing unit from the pixel level to the row/column level. The security of the new plot is accomplished through a substitution permutation network, where we apply a circular shift of rows and columns to break the solid connection of neighboring pixels. At that point, we join the XOR operation with modulo function to cover the pixels values and forestall any leaking of data. High-security tests and simulation analyses are carried out to exhibit that the scheme is very secure and exceptionally quick for real-time image processing at 80 fps (frames per second).