Background: Lung cancer remains a significant health concern, and the effectiveness of early detection significantly enhances patient survival rates. Identifying lung tumors with high precision is a challenge due to the complex nature of tumor structures and the surrounding lung tissues. Methods: To address these hurdles, this paper presents an innovative three-step approach that leverages Generative Adversarial Networks (GAN), Long Short-Term Memory (LSTM), and VGG16 algorithms for the accurate reconstruction of three-dimensional (3D) lung tumor images. The first challenge we address is the accurate segmentation of lung tissues from CT images, a task complicated by the overwhelming presence of non-lung pixels, which can lead to classifier imbalance. Our solution employs a GAN model trained with a reinforcement learning (RL)-based algorithm to mitigate this imbalance and enhance segmentation accuracy. The second challenge involves precisely detecting tumors within the segmented lung regions. We introduce a second GAN model with a novel loss function that significantly improves tumor detection accuracy. Following successful segmentation and tumor detection, the VGG16 algorithm is utilized for feature extraction, preparing the data for the final 3D reconstruction. These features are then processed through an LSTM network and converted into a format suitable for the reconstructive GAN. This GAN, equipped with dilated convolution layers in its discriminator, captures extensive contextual information, enabling the accurate reconstruction of the tumor’s 3D structure. Results: The effectiveness of our method is demonstrated through rigorous evaluation against established techniques using the LIDC-IDRI dataset and standard performance metrics, showcasing its superior performance and potential for enhancing early lung cancer detection. Conclusions:This study highlights the benefits of combining GANs, LSTM, and VGG16 into a unified framework. This approach significantly improves the accuracy of detecting and reconstructing lung tumors, promising to enhance diagnostic methods and patient results in lung cancer treatment.

Objective. Pre-participation medical screening of athletes is necessary to pinpoint individuals susceptible to cardiovascular events. Approach. The article presents a reinforcement learning (RL)-based multilayer perceptron, termed MLP-RL-CRD, designed to detect cardiovascular risk among athletes. The model underwent training using a publicized dataset that included the anthropological measurements (such as height and weight) and biomedical metrics (covering blood pressure and pulse rate) of 26 002 athletes. To address the data imbalance, a novel RL-based technique was adopted. The problem was framed as a series of sequential decisions in which an agent classified a received instance and received a reward at each level. To resolve the insensitivity to the initialization of conventional gradient-based learning methods, a mutual learning-based artificial bee colony (ML-ABC) was proposed. Main Results. The model outcomes were validated against positive (P) and negative (N) ECG findings that had been labeled by experts to signify individuals ‘at risk’ and ‘not at risk,’ respectively. The MLP-RL-CRD approach achieves superior outcomes (F-measure 87.4%; geometric mean 89.6%) compared with other deep models and traditional machine learning techniques. Optimal values for crucial parameters, including the reward function, were identified for the model based on experiments on the study dataset. Ablation studies, which omitted elements of the suggested model, affirmed the autonomous, positive, stepwise influence of these components on performing the model. Significance. This study introduces a novel, effective method for early cardiovascular risk detection in athletes, merging reinforcement learning and multilayer perceptrons, advancing medical screening and predictive healthcare. The results could have far-reaching implications for athlete health management and the broader field of predictive healthcare analytics.

Attention as a cognition ability plays a crucial role in perception which helps humans to concentrate on specific objects of the environment while discarding others. In this paper, auditory attention detection (AAD) is investigated using different dynamic features extracted from multichannel electroencephalography (EEG) signals when listeners attend to a target speaker in the presence of a competing talker. To this aim, microstate and recurrence quantification analysis are utilized to extract different types of features that reflect changes in the brain state during cognitive tasks. Then, an optimized feature set is determined by employing the processes of significant feature selection based on classification performance. The classifier model is developed by hybrid sequential learning that employs Gated Recurrent Units (GRU) and Convolutional Neural Network (CNN) into a unified framework for accurate attention detection. The proposed AAD method shows that the selected feature set achieves the most discriminative features for the classification process. Also, it yields the best performance as compared with state-of-the-art AAD approaches from the literature in terms of various measures. The current study is the first to validate the use of microstate and recurrence quantification parameters to differentiate auditory attention using reinforcement learning without access to stimuli.