Myocarditis is heart muscle inflammation that is becoming more prevalent these days, especially with the prevalence of COVID‐19. Noninvasive imaging cardiac magnetic resonance (CMR) can be used to diagnose myocarditis, but the interpretation is time‐consuming and requires expert physicians. Computer‐aided diagnostic systems can facilitate the automatic screening of CMR images for triage. This paper presents an automatic model for myocarditis classification based on a deep reinforcement learning approach called as reinforcement learning‐based myocarditis diagnosis combined with population‐based algorithm (RLMD‐PA) that we evaluated using the Z‐Alizadeh Sani myocarditis dataset of CMR images prospectively acquired at Omid Hospital, Tehran. This model addresses the imbalanced classification problem inherent to the CMR dataset and formulates the classification problem as a sequential decision‐making process. The policy of architecture is based on convolutional neural network (CNN). To implement this model, we first apply the artificial bee colony (ABC) algorithm to obtain initial values for RLMD‐PA weights. Next, the agent receives a sample at each step and classifies it. For each classification act, the agent gets a reward from the environment in which the reward of the minority class is greater than the reward of the majority class. Eventually, the agent finds an optimal policy under the guidance of a particular reward function and a helpful learning environment. Experimental results based on standard performance metrics show that RLMD‐PA has achieved high accuracy for myocarditis classification, indicating that the proposed model is suitable for myocarditis diagnosis.

Three‐dimensional (3D) image reconstruction of tumours can visualise their structures with precision and high resolution. In this article, GAN‐LSTM‐3D method is proposed for 3D reconstruction of lung cancer tumours from 2D CT images. Our method consists of three phases: lung segmentation, tumour segmentation, and tumour 3D reconstruction. Lung segmentation is done using snake optimisation followed by tumour segmentation using Gustafson‐Kessel (GK) clustering method. The outputs of GK (2D lung cancer images) are fed to a pre‐trained Visual Geometry Group (VGG) for feature extraction. The VGG outputs are used as input for an attention‐based LSTM which performs feature unpacking. The output of LSTM units is given to generator network of a Generative Adversarial Networks (GAN) model to carry out 3D reconstruction of (normal/cancerous) images with high quality. During training, the discriminator network of the GAN is used to judge the generator outputs. The authors to the best of their knowledge were the first to use GAN for 3D reconstruction of lung cancer tumours which is the primary contribution of this article. Moreover, existing studies are mostly focused on brain tumours and are not suitable for lung tumour reconstruction. Focusing on lung tumours is the second contribution of this article. Evaluation on LUNA data collection against existing methods like MC, MC + fairing etc. reveals the superiority of our method in terms of Hamming and Euclidean distance metrics. Additionally, the computational complexity of the proposed method is lower compared to evaluated methods.

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.

CMRI is the exclusive imaging technique capable of identifying myocardial edema, endomyocardial fibrosis, pericarditis accompanied by pericardial effusions, and apical thrombi within either the left or right ventricle. In this work, we examine the research literature on the use of CMRI in the diagnosis of chest discomfort, employing randomized controlled trials (RCTs) to evaluate its effectiveness. The research outlines the disorders of the chest and the machine learning approaches for detecting them. In conclusion, the study ends with an examination of a fundamental illustration of CMRI analysis. To find a comprehensive review, the Scopus scientific resource is analyzed. The issue, based on the findings, is to distinguish ischemia from non-ischemic cardiac causes of chest pain in individuals presenting with sudden chest pain or discomfort upon arrival at the emergency department (ED). Due to the failure of conventional methods in accurately diagnosing acute cardiac ischemia, individuals are still being inappropriately discharged from the ED, resulting in a heightened death rate.

Precise predictions of real estate taxes (RETs) are critical for property owners and government agencies because they influence economic planning and public revenue generation. Traditional deep learning methods for RET often struggle with feature selection and are highly sensitive to hyperparameter settings. To overcome these issues, we present a sophisticated predictive model for RET that incorporates a refined reinforcement learning (RL) technique, termed off-policy proximal policy optimization (PPO), which excels in feature selection, and with an advanced differential evolution (DE) algorithm for optimal hyperparameter tuning. The off-policy PPO component continually updates its feature selection based on data interactions, sharpening its focus on crucial features and preventing overfitting. Moreover, the DE algorithm has been enhanced with a novel mutation strategy that uses k-means clustering to identify key clusters. We tested our model on diverse real estate datasets from Kaggle, covering locations such as Bucharest, California, Helsinki, King County, and Saudi Arabia. Our findings show that our model significantly outperforms traditional pricing tools, with mean absolute percentage error (MAPE) ranging from 0.202 to 1.362 across various datasets. This superior performance underscores the broad applicability of the model across markets, markedly improving financial analysis and strategic planning.