An Efficient Combination of Convolutional Neural Network and LightGBM Algorithm for Lung Cancer Histopathology Classification.

The most dangerous disease in recent decades is lung cancer. The most accurate method of cancer diagnosis, according to research, is through the use of histopathological images that are acquired by a biopsy. Deep learning techniques have achieved success in bioinformatics, particularly medical imaging.

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.

Directed jaya algorithm for delivering nano-robots to cancer area.

In the last few years, it was proposed to deliver drugs using Nano-robots for treating cancer. This paper compares between two recent and efficient algorithms for delivering Nano-robots to cancer area. These algorithms are Jaya algorithm and Directed Particle Swarm Optimization (DPSO) algorithm.

EEG-EOG based Virtual Keyboard: Toward Hybrid Brain Computer Interface.

The past twenty years have ignited a new spark in the research of Electroencephalogram (EEG), which was pursued to develop innovative Brain Computer Interfaces (BCIs) in order to help severely disabled people live a better life with a high degree of independence. Current BCIs are more theoretical than practical and are suffering from numerous challenges. New trends of research propose combining EEG to other simple and efficient bioelectric inputs such as Electro-oculography (EOG) resulting from eye movements, to produce more practical and robust Hybrid Brain Computer Interface systems (hBCI) or Brain/Neuronal Computer Interface (BNCI).

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.