Journal of Artificial Neural Networks and Learning System (p-ISSN: 3049-0758, e-ISSN: 3048-6629)

Multi-Class Lung Disease Detection Using Attention-Based Deep Learning on Chest X-Ray Images

2026-06-29T13:26:02+00:00

The increasing prevalence of respiratory diseases such as COVID-19, pneumonia, and tuberculosis has significantly impacted global healthcare systems, necessitating the development of efficient, automated diagnostic solutions. Chest X-ray (CXR) imaging is one of the most widely used diagnostic tools due to its affordability, accessibility, and rapid acquisition. However, accurate multi-class classification of lung diseases remains a challenging problem because of overlapping radiographic features and variations in image quality. This paper proposes a comprehensive deep learning-based framework for multi-class lung disease detection using attention mechanisms and hierarchical classification. The proposed system integrates lung region segmentation using a U-Net architecture to isolate relevant anatomical structures and remove background noise. An attention-enhanced convolutional neural network (CNN) is employed to extract discriminative features, focusing on disease-specific regions within the lungs. Furthermore, a hierarchical classification strategy is adopted to first distinguish between normal and abnormal cases, followed by fine-grained classification of specific lung diseases. To improve model interpretability, Grad- CAM visualization is incorporated to highlight the regions influencing the model’s predictions. Experimental results demonstrate that the proposed system significantly improves classification accuracy, reduces misclassification among similar diseases, and enhances interpretability. The framework offers a reliable and efficient computer-aided diagnosis system that can support radiologists in clinical decision-making.

Deep Learning and AI Approaches for Autonomous Mobile Robot Navigation: A Simulation-Based Study with Real-World Deployment Perspectives

2026-06-30T12:19:41+00:00

Autonomous mobile robot navigation is a core research area in artificial intelligence and robotics, enabling robots to operate effectively in complex and dynamic real-world environments. Traditional navigation approaches based on geometric modeling and rule-based planning often fail to generalize in unstructured and uncertain scenarios. In contrast, recent advances in deep learning and reinforcement learning have significantly improved perception, decision-making, and control capabilities in autonomous systems. This study proposes a hybrid AI-based navigation framework that integrates convolutional neural networks (CNNs) for perception, Simultaneous Localization and Mapping (SLAM) for state estimation, and Proximal Policy Optimization (PPO)-based deep reinforcement learning for motion planning and control. The system also incorporates sensor fusion to enhance robustness under noisy and dynamic conditions. Experimental results obtained in a ROS-Gazebo simulation environment demonstrate a navigation success rate of 94%, with substantial reductions in collision rate and execution time compared to classical methods. The findings confirm that hybrid AI architectures significantly enhance adaptability, robustness, and real-time performance in autonomous navigation tasks, although challenges such as sim-to-real transfer, safety assurance, and data efficiency remain open research problems.

Liver Disease Prediction using Multi-Layer Perceptron (MLP) Deep Learning Technique

2026-06-06T14:35:23+00:00

As liver diseases are the causes of many health issues in the world, there is a need for accurate and efficient diagnostic methods to detect liver diseases as early as possible. In this study, a model for the classification of liver diseases using Multi-Layer Perceptron (MLP) Neural Network is developed, which is good at capturing complex and non-linear relationships in data sets. The training and testing used data comprising information on bilirubin, enzymes, and patient characteristics for liver disease. Normalization, feature selection, and discarding outliers are used to increase the accuracy of the model and avoid it becoming too fitting for the training data. It has several hidden layers with ReLU activation, and Adam is used to optimize its parameters. Common ways to assess the model are by looking at its accuracy, precision, recall, F1-score, and ROC-AUC. The experimental results reveal that the proposed MLP model has an overall accuracy of 92.26% while the accuracy of conventional machine learning algorithms such as decision tree, support vector machine and KNN are 78.57%, 83.47% and 76.32%, respectively. Moreover, the model exhibits good generalization performance on various subsets of data and is therefore relevant for clinical use. Doctors can benefit from the use of MLPs to make faster and more accurate diagnosis of liver diseases, thereby improving patients' health outcomes. Future work aims at connecting the model to real-time clinical decision aids for clinical management and further developing the framework to enable the detection of specific liver disease modalities as Hepatitis, Cirrhosis, fatty liver disease etc.