Jul 2026· Journal of Intelligent Decision Making and Information Science· Vol 3, pp. 353-376· 1 citation· 29 references
TL;DR
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.
Abstract
Given the growing challenges in healthcare diagnostics, the proposed research study aims to transform how we identify lung diseases. We aim to achieve this by integrating AI, Large Language Models (LLMs), and Image Processing with medical expertise. The main driving force behind the initiative is the need for more accurate diagnoses. In this regard, the primary focus is on using lung X-ray images to harness healthcare through image processing. Additionally, 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. This interactive feature enhances collaboration between AI and medical professionals, creating a platform for exchanging knowledge. A carefully designed convolutional neural network (CNN) was investigated, achieving an Area Under the Curve-Receiver Operating Characteristic (AUC-ROC) of 90%. By doing so, the study not only aligns with Saudi Arabia's vision for technological advancement in healthcare by 2030 but also aims to alleviate burdens on the healthcare sector and enhance overall quality of life. The proposed approach represents a collaboration that promises groundbreaking advancements in the diagnostic process.
Pneumonia remains a major cause of lung disease globally, and its timely and reliable diagnosis is crucial. Radiology is often used to detect the infection in a chest X-ray, but this process can be laborious and depend on the radiologist’s interpretation. However, recent advances in deep learning techniques have demonstrated high accuracy in automated pneumonia detection, but their "black-box" nature hampers their practical use. Health-care professionals often need to understand the reasons behind predictions to trust the automated system. This study introduces a machine learning-based approach with additional explainable techniques to enhance model performance and explainability in predicting pneumonia. The proposed method uses a convolutional neural network to predict chest X-ray images, and explainability techniques like Grad-CAM and SHAP are used to explain which parts of the image contribute most to the prediction. We test the system on the RSNA Pneumonia Detection Challenge Dataset that includes expert-provided infection labels. The proposed explanation method is evaluated by comparing the model’s predictions with expert annotations. This study demonstrates that our system not only provides accurate classification results but also provides plausible visual explanations that correspond to the desired locations in the chest. This approach can help boost trust in AI-driven medical diagnostic systems and enable their potential deployment in clinical practice.
This project introduces an AI-powered diagnostic platform designed to identify major respiratory conditions from chest X-ray images using a advanced hybrid Deep Learning architecture. By integrating YOLOv8 for precise lesion localization and ResNet50 for deep feature extraction, the system overcomes the limitations of traditional single-model approaches, offering a more detailed analysis of lung pathology. The model is trained on a comprehensive dataset encompassing four critical categories: Normal, COVID-19, Pneumonia, and Tuberculosis. To ensure clinical reliability, the system employs advanced preprocessing including normalization and augmentation to handle variations in X-ray quality. This dual-network engine is integrated into a responsive web application that provides healthcare providers with near-instantaneous diagnostic results and confidence scores. With a user-friendly interface designed for both specialists and general practitioners, the platform bridges the gap in medical expertise, particularly in resource-limited or remote regions. By combining automated detection with accessible web technology, this research provides a scalable solution to accelerate clinical decision-making and improve patient outcomes in respiratory healthcare.
A. B, M. S, P. N et al.· 2026 6th International Confe...· 0 citations
Chest radiograph images have become a critical research area for applying deep learning in radiological interpretation for the classification of pulmonary diseases. But, to achieve both high accuracy and good interpretability continues to be a major hurdle for many researchers. In this research, we offer a hybrid architecture that incorporates CNNs and Transformer techniques for classifying different respiratory diseases using chest radiograph images. The CNN component provides a mechanism to capture many of the fine, local details found in an image, while the Transformer provides a self-attentive mechanism to capture the overall context of an X-ray image. In addition, a range of approaches exist to improve overall performance of the CNN and Transformer architecture, including structured preprocessing, data augmentation and class balancing. All of these techniques will improve model learning performance and help to effectively manage class imbalance when dealing with imbalanced datasets. To make our model more transparent to users and clinically useful, we employed explainability methods like Grad-CAM and Attention Visualizations to provide users with evidence of the specific area in an X-ray where the model is basing its prediction, thereby providing a greater amount of trust on the part of radiologists in interpreting the model's output. Based on our findings from testing the 6 Classes Chest Xray dataset, the proposed system proved to achieve a very impressive final testing accuracy of 94.42%. It classifies tuberculosis and healthy patients particularly well, with precision, recall, and F1-scores of 0.99 and 0.97, respectively, but provides good performance across the other disease types too. Furthermore, confidence analysis of predicted labels exhibited that when there was an accurate prediction, the assigned probability score was usually much higher than the assigned probability score for an incorrect prediction. Thus, these results suggest that the hybrid CNN-Transformer model provides a strong level of diagnostic accuracy and meaningfully understood visual rationale so it can serve as an excellent decision support mechanism for hospitals and radiologists in their daily operations.
Prasanna Pabba, N. S. Chaitanya, M. Ravikanth et al.· Journal of Intelligent Decis...· 0 citations
Due to its late identification and challenging diagnosis, lung cancer continues to be one of the top causes of death for cancer patients globally, positioning it as one of the most critical concerns. Timely identification of cancerous nodules is essential for enhancing the patient’s survival likelihood CT image analysis by hand is not very productive and significantly relies on a specialist’s expertise. In this study, we offer an autonomous lung cancer classification method based on explainable deep learning. The popular DenseNet121 network serves as the foundation for our deep learning model, which is enhanced by the Convolutional Block Attention Module (CBAM). To improve feature extraction of significant spatial and channel properties of input data, attention techniques are added. Furthermore, our method is interpretable because the Grad-CAM technique makes it possible to explain the choices made by a machine learning system. A database of CT scans, comprising 4,598 pictures categorized by large cell carcinoma, adenocarcinoma, and healthy lungs, was utilized. Our evaluations show the model’s effectiveness with an accuracy rate of 94.6\%.
S. Jegadeesan, S. Matheswaran, R. Palanivelrajan· International Conference on...· 0 citations
One of the most common and deadly infectious illnesses in the world is still tuberculosis (TB), especially in developing nations with inadequate healthcare systems. In order to stop the spread of tuberculosis and enhance patient outcomes, early identification and diagnosis are essential. In this study, we present a deep learning-based system that uses chest X-ray pictures to automatically detect tuberculosis. Despite the difficulties of limited dataset availability, the system uses transfer learning using MobileNetV2 and DenseNet architectures to classify chest Xrays as either TB-positive or Healthy, reaching notable accuracy. To increase model generalisation and image quality, pre-processing methods like Contrast Limited Adaptive Histogram Equalisation (CLAHE) and sophisticated data augmentation approaches are used. The trained model is then implemented as a Flask web application, offering a user-friendly interface with features like secure login, image upload and preview, prediction results with probability scores, and performance metrics visualisation like accuracy curves, confusion matrices, and ROC curves. The suggested framework shows how deep learning can be used to create scalable, dependable, and affordable diagnostic tools to help radiologists and other medical professionals with TB screening and diagnosis.
Zoya Nasreen, Afshan Fatima, Ruqiya Fatima· International Journal of AI...· 0 citations
Background: Pneumonia remains a leading cause of mortality worldwide, with chest X-ray serving as the primary diagnostic tool. However, manual interpretation is subject to inter-observer variability, and existing deep learning models often require substantial computational resources that limit deployment in resource-constrained clinical environments.
Objective: This study aimed to develop Light CNN, a novel lightweight convolutional neural network that integrates depthwise separable convolution, inverted residual blocks, channel shuffle mechanism, and lightweight attention for efficient and accurate pneumonia classification from chest X-ray images.
Methods: Light CNN was designed with seven progressive feature extraction stages that incorporate the four aforementioned optimization techniques. The model was trained and evaluated on the publicly available Chest X-Ray Images (Pneumonia) dataset from Kaggle, comprising 5,856 images stratified into training (70%), validation (15%), and test (15%) subsets with patient-level splitting to prevent data leakage.
Preprocessing included CLAHE contrast enhancement, normalization, and data augmentation. Training employed the AdamW optimizer with cosine annealing scheduling and class-weighted cross-entropy loss over 50 epochs. The performance of Light CNN was benchmarked against three baseline models — MobileNetV2 (2.23 M parameters), ResNet-18 (11.18 M parameters), and EfficientNet-B0 (4.01 M parameters) — using identical preprocessing and training protocols. Evaluation metrics included accuracy, precision, recall, F1-score, AUC-ROC, parameter count, model size, and inference time.
Results: LightCNN achieved 95.56% accuracy, 0.9556 recall, 0.9584 precision, 0.9562 F1-score, and 0.9875 AUC-ROC on the test set, outperforming all baseline models. The model contains 2.52 million parameters (9.63 MB), representing a 77.4% reduction compared to ResNet-18, with an inference time of 0.25 ms per image — approximately four times faster than the nearest competitor. Ablation study results confirmed that each architectural component contributed incrementally to overall performance; depth wise separable convolution provided the largest efficiency gain, and inverted residual blocks contributed the most substantial accuracy improvement.
Conclusion: Light CNN demonstrates that systematic integration of lightweight architectural techniques can achieve clinically relevant diagnostic performance with minimal computational overhead, supporting its potential deployment in mobile and edge computing scenarios for point-of-care pneumonia diagnosis.
W. Swastika, Heri Kristianto, Paulus Lucky Tirma Irawan et al.· Health Sciences Investigatio...· 0 citations