AUTOMATED CHEST X-RAY REPORT GENERATION USING ARTIFICIAL INTELLIGENCE BASED ATTENTION-ENHANCED GOOGLENET-LSTM ARCHITECTURE
DOI:
https://doi.org/10.15588/1607-3274-2026-2-9Keywords:
Convolution Neural Networks, Deep Learning, Chest X-ray, Radiology Report Generation, GoogleNet, LSTM, Attention MechanismAbstract
Context. Proper and effective diagnosis of chest diseases is vital in timely treatment and efficient clinical decision-making. Chest X-rays (CXRs) are commonly utilized in the detection of chest diseases because they are readily accessible and cost-effective. Nevertheless, radiograph interpretation is still a time-consuming, subjective, and error-prone process, especially in health care settings where resources are limited. Radiologists require automated systems capable of producing consistent diagnostic information that will facilitate quicker and standardized patient care.
Objective. The proposed research will develop and assess a deep learning model with the ability to produce valid and explainable diagnostic reports using chest radiographs. The main aim is to minimize human error, save time in the diagnostic process, and deliver uniform findings, which will guide clinicians to make sound decisions within a short period of time.
Method. The images are then extracted using a convolutional neural network called GoogleNet that extracts high-level visual features, which contain important structural and anatomical information. The features extracted are then fed to a Long Short-Term Memory network, which represents the sequential character of the report generation process by conditioning itself on relationships between words and phrases in diagnostic text. To enhance its accuracy and interpretability, an attention mechanism is added to allow the system to concentrate on the most clinically valuable parts of the image when producing every part of the report. The Indiana University Chest X-ray dataset was employed to train and evaluate the proposed system, while several experiments were carried out to evaluate the performance of the proposed system regarding its performance against the existing benchmark models.
Results. The GoogleNet-LSTM-Attention model was shown to be more effective at generating high-quality diagnostic reports. It has performed well compared to benchmark models on various natural language evaluation measures, such as BLEU, ROUGE, and CIDEr scores. These improvements indicate that the quality of clinical data and fluency of text generated are correlated and that CNN, RNN, and attention mechanisms are effective in medical image reporting.
Conclusions. The study presented shows that a combination of CNNs, LSTMs, and attention in a single architecture has the potential to transform the process of interpreting chest X-rays. The system proposed not only improves the precision of the diagnosis but also provides clinical assistance, as it allows for performing radiographic assessment rapidly, consistently, and interpretably. Such AI-driven systems can bring about the potential to reduce workloads, decrease diagnostic errors, and enhance patient outcomes in various healthcare facilities.
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