Lung cancer is a leading cause of cancer-related deaths worldwide, emphasizing the significance of early detection. Computer-aided diagnostic systems have emerged as valuable tools for aiding radiologists in the analysis of medical images, particularly in the context of lung cancer screening. A typical pipeline for lung cancer diagnosis involves pulmonary nodule detection, segmentation, and classification. Although traditional machine learning methods have been deployed in the previous years with great success, this literature review focuses on state-of-the-art deep learning methods. The objective is to extract key insights and methodologies from deep learning studies that exhibit high experimental results in this domain. This paper delves into the databases utilized, preprocessing steps applied, data augmentation techniques employed, and proposed methods deployed in studies with exceptional outcomes. The reviewed studies predominantly harness cutting-edge deep learning methodologies, encompassing traditional convolutional neural networks (CNNs) and advanced variants such as 3D CNNs, alongside other innovative approaches such as Capsule networks and transformers. The methods examined in these studies reflect the continuous evolution of deep learning techniques for pulmonary nodule detection, segmentation, and classification. The methodologies, datasets, and techniques discussed here collectively contribute to the development of more efficient computer-aided diagnostic systems, empowering radiologists and dfhealthcare professionals in the fight against this deadly disease.

The field of brain–computer interface (BCI) enables us to establish a pathway between the human brain and computers, with applications in the medical and nonmedical field. Brain computer interfaces can have a significant impact on the way humans interact with machines. In recent years, the surge in computational power has enabled deep learning algorithms to act as a robust avenue for leveraging BCIs. This paper provides an up-to-date review of deep and hybrid deep learning techniques utilized in the field of BCI through motor imagery. It delves into the adoption of deep learning techniques, including convolutional neural networks (CNNs), autoencoders (AEs), and recurrent structures such as long short-term memory (LSTM) networks. Moreover, hybrid approaches, such as combining CNNs with LSTMs or AEs and other techniques, are reviewed for their potential to enhance classification performance. Finally, we address challenges within motor imagery BCIs and highlight further research directions in this emerging field.

The Q-problem is a new lightweight and hard mathematical problem that resists quantum attacks. It depends on putting one known value and two unknown values per equation; whatever the operator, the Q-problem defines certain conditions between equations. This paper presents a new key exchange protocol based on the Q-problem. To protect secure end-to-end communication over a public transmission channel, the proposed mechanism consists of two rounds of exchanging totally random numbers, which ensure a shared secret key between two parties at the end. Security analysis proves the robustness of the proposal and experiments prove its lightness during implementation, making it a promising protocol of hybrid solutions and an assistive technique for the transition to the quantum era.

Nowadays, while steganography is the main mean of illegal secret communication, the need of detecting steganographic content and especially stego images is becoming more compulsory. Since multimedia content can be easily spread over the internet and more complicated steganography algorithms in different domains i.e. spatial, transform are utilized, the task of identifying stego images becomes very difficult. Early steganalysis methods deploy statistical attacks on stego images while more recent ones use deep learning techniques. The latter ones mainly utilize convolutional neural networks and show promising results. In this paper we propose a novel method to identify stego images derived from two different steganographic algorithms S-UNIWARD (Spatial-UNIversal WAvelet Relative Distortion) and WOW (Wavelet Obtained Weights) for various embedding rates. The proposed method initially utilizes a dilated convolutional neural network as a feature extractor and afterwards the extracted feature vector trains a random forest classifier. More specifically it is proved that in steganalysis, a dilated convolutional neural network could be an excellent feature extractor and the traditional softmax layer could be replaced by another machine learning classifier. Extensive experiments were conducted, and the proposed model was also compared against state-of the-art convolutional neural networks utilized in spatial image steganalysis, and other feature extraction methods. Results showed that the proposed method achieves high classification accuracy and outperforms other analogous steganalysis approaches.

Lung cancer is by far the leading cause of cancer death among both men and women, making up almost 25% of all cancer deaths Each year, more people die of lung cancer than colon, breast, and prostate cancer combined. The early detection of lung cancer is critical for improving patient outcomes, and automation through advanced image analysis techniques can significantly assist radiologists. This paper presents the development and evaluation of a computer-aided diagnostic system for lung cancer screening, focusing on pulmonary nodule segmentation in low-dose CT images, by employing HighRes3DNet. HighRes3DNet is a specialized 3D convolutional neural network (CNN) architecture based on ResNet principles which uses residual connections to efficiently learn complex spatial features from 3D volumetric data. To address the challenges of processing large CT volumes, an efficient patch-based extraction pipeline was developed. This method dynamically extracts 3D patches during training with a probabilistic approach, prioritizing patches likely to contain nodules while maintaining diversity. Data augmentation techniques, including random flips, affine transformations, elastic deformations, and swaps, were applied in the 3D space to enhance the robustness of the training process and mitigate overfitting. Using a public low-dose CT dataset, this approach achieved a Dice coefficient of 82.65% on the testing set for 3D nodule segmentation, demonstrating precise and reliable predictions. The findings highlight the potential of this system to enhance efficiency and accuracy in lung cancer screening, providing a valuable tool to support radiologists in clinical decision-making.