YOLO Based Breast Masses Detection and Classification in Full-Field Digital Mammograms.

Background And Objective: With the recent development in deep learning since 2012, the use of Convolutional Neural Networks (CNNs) in bioinformatics, especially medical imaging, achieved tremendous success. Besides that, breast masses detection and classifications in mammograms and their pathology classification are considered a critical challenge. Till now, the evaluation process of the screening mammograms is held by human readers which is considered very monotonous, tiring, lengthy, costly, and significantly prone to errors.

Hybrid, randomized and high capacity conservative mutations DNA-based steganography for large sized data.

In this paper, a well secured, high capacity, preserved algorithm is proposed through integrating the cryptography and steganography concepts with the molecular biology concepts. We achieved this by first encrypting the confidential data using the DNA Playfair cipher to avoid extra information sent to the receiver and it consequently acts as a trap for an attacker. Second, it achieves a randomized steganography process by exploiting the DNA conservative mutations.

Color image segmentation based on different color space models using automatic GrabCut.

This paper presents a comparative study using different color spaces to evaluate the performance of color image segmentation using the automatic GrabCut technique. GrabCut is considered as one of the semiautomatic image segmentation techniques, since it requires user interaction for the initialization of the segmentation process. The automation of the GrabCut technique is proposed as a modification of the original semiautomatic one in order to eliminate the user interaction.