The combination of convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to analyses patient data and medical images and makes the knowledge about how these models may be used to assist in the early diagnosis and pre-treatment plans.
The research introduces a dimension in which medical specialists can have real-time conversations with a trained LLM model that leverages knowledge from a medical encyclopedia, and enhances collaboration between AI and medical professionals, creating a platform for exchanging knowledge.
Mohammed Imran Basheer Ahmed· Journal of Intelligent Decis...· 1 citation
Background: Medical imaging has innovatively changed the healthcare set-up through the facilitation of early and non-invasive analysis. Despite all these, the developing complexity of imaging information needs qualitative diagnosis tools. Although deep learning, especially with the use CNNs, has indicated possible, challenges such as data dependency and interpretation may hamper its clinical acceptability.
Objective: The major focus of this study may add physical limitations to CNNs to improve consistency and healthcare organizational adoption of deep learning frameworks. The main scope of this work may include the growth of a physics-Informed Convolutional Neural Network (Pi-CNN) for sort of brain tumour categorization from MRI images and followed by its testing framework.
Methodology: Scientists employed baseline CNN model and Pi-CNN with the same parameters to MRI brain tumor exercise procedures. With spatial consonance limitation being integrated into the Pi-CNN it can enable the medical image forecast through the method of anatomy. Homogenous metric evaluations were carried out on their experimentation plan by several researchers who applied the two different model types.
Results: At the testing stage, the Baseline CNN may set out to attain a slightly better precision rate at 84% than Pi-CNN which achieved at 80.0%. Information detection precision and generalization abilities were better in the Pi-CNN which affirmed that the status is the good choice for the purpose of medical application.
Specific Contribution: The precise input delivers a replication to Physics-Informed CNN model, which maintains a precise results whereas tempting to enhance the quality of the results and clinical trust phases. The base framework will enable deep learning approaches at numerous levels to combine clinical perfection with outcomes which can enhance diagnostic imaging performance.
Conclusion: Medical professionals applying limitations for CNNs identified enhancement in more efficient models which can enhance clinical utility and consistency. Future AI model innovation may involve improved pre-existing purview of knowledge of biological pedigree for physics in connection with enhanced health record translation because of the presumed combination and it will assist physicians in their investigation procedures.
Unknown authors· Al-Noor Journal of Engineeri...· 0 citations
Renal pathology represents a diverse set of diseases that present significant clinical relevance. Included among the various types of renal pathologies are renal stones, cysts, and renal malignancies, all of which require diagnosis and therapy to prevent progression of the disease process. The current research study was performed to create and validate a classification model based on deep learning using a convolutional neural networks (CNN) architecture, namely a 50-layer Residual Network (ResNet-50) using Gradient-weighted Class Activation Mapping (Grad-CAM), to provide improved automatic detection of renal pathology from medical images and improve the interpretability of those medical images. During the study, the Explainable Deep Learning Pipeline (X-DLP) paradigm was followed, which provides a structured methodology to perform research with the use of deep learning in medical imaging. The X-DLP structures the research process into a series of phases, including Data acquisition and curation, Preprocessing and Augmentation, Model Creation via Transfer Learning, and lastly, Interpretability and Visualization.The results obtained show that the proposed model performs consistently well across different evaluation metrics. The Precision–Recall curve, with a PR-AUC close to 0.89, suggests that the model is effective at identifying positive cases even when the data are imbalanced. In addition, the F1-score reaches a peak of around 0.835 at a threshold near 0.45, indicating a good trade-off between precision and recall. From another perspective, the evaluation using Youden’s criterion reveals sensitivity and specificity values close to 0.80, which supports the model’s ability to distinguish between classes with reasonable accuracy. Moreover, the lift and cumulative gain analysis further highlight its practical usefulness, with a lift of 3.5 in the top 10% and a cumulative gain of 75% when considering 30% of the population. These results indicate that the model can effectively prioritize the most relevant positive cases. Overall, these findings suggest that the model can serve as a valuable support tool in medical diagnosis. By enabling automated classification of renal images and providing visual insights through interpretability techniques, it helps streamline clinical decision-making, reduces reliance on purely manual assessments, and enhances its potential for real-world application.
L. Andrade-Arenas, Inooc Rubio Paucar, Cesar Yactayo-Arias· International Journal of Adv...· 0 citations
Empirical evidence from recent studies demonstrates that fine-tuning and network-based DTL strategies, including federated learning, consistently enhance diagnostic accuracy, robustness, and generalization across multiple medical imaging modalities, particularly in data-limited clinical scenarios.
M. A. S. Banu, A. Dhavapandiammal, K. Palanisamy· Current medical imaging· 0 citations
This comprehensive review systematically examines the architecture, functionality, and clinical effectiveness of ANN-based models applied to a diverse spectrum of medical imaging modalities, encompassing mammography, magnetic resonance imaging (MRI), computed tomography (CT), fundus photography, and dermoscopy.
Maryam Omar Al-Tohamy, Abdel Hamid, A. Arjiah et al.· Al-Farooq Journal of Science...· 0 citations
Lung cancer remains among the leading causes of mortality in the world and CT imaging is one of the crucial instruments of early diagnosis. These scans however are time consuming and prone to error in case they are manually interpreted. We use a dataset of 364 lung CT images in this study, as they have been obtained in an Iranian hospital comprising of 238 cancerous and 126 noncancerous patients, which are accorded with labels by a specialist in pulmonology. We test five different convolutional neural network designs, under the same preprocessing, augmentation and training regimes to give an equal evaluation opportunity. To solve the issue of class imbalance and low number of samples, data augmentation methods were used to increase the size of the data threefold to enhance model generalization. To make the decisions of the models more interpretable, saliency maps and Grad Cam were employed to visualize the decision-making of the models, where attention was paid to the clinically relevant areas, including tumor areas in cancerous scans and ground glass opacities in noncancerous images. The results of the experiment suggest that NASNet Mobile demonstrates excellent performance, with the highest level of test accuracy of 99.94. These results indicate that it is more effective in classifying lung cancer. In general, this paper delivers a multimodal comparison, solid data augmentation approaches, and interpretable visual features that enhance clinical confidence. It leads to the development of automated lung cancer detection, as well as offering a scalable solution that can be deployed in resource constrained settings.
Md Sujon Ali, M. Ahad, Fabiha Faiz Mahi et al.· 2026 International Conferenc...· 0 citations
