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Enhancing lung diseases recognition through CNN-RNN methodologies.

Israt Ara Zahin¹, Md Fardin Ahsan², Ramisa Anan Orni²

¹School of Data and Sciences, BRAC University, Dhaka, Bangladesh. israt.ara.zahin@g.bracu.ac.bd.

Scientific Reports

|April 2, 2026

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A new deep learning model, Convolutional Recurrent Network (C-RNet), accurately detects lung diseases like pneumonia and tuberculosis from X-ray images. This advanced method shows superior diagnostic accuracy compared to existing techniques.

Area of Science:

Medical Imaging and Artificial Intelligence
Radiology and Diagnostic Medicine

Background:

Medical imaging, particularly X-rays, is crucial for diagnosing respiratory disorders by revealing lung anomalies.
Deep Convolutional Neural Networks (DCNNs) show promise in enhancing lung illness recognition from X-ray images.

Purpose of the Study:

To develop and evaluate a deep learning (DL) algorithm for detecting lung diseases from chest X-ray (CXR) images.
To classify CXR images into four categories using a novel DL-based approach.

Main Methods:

Proposed a deep learning model named Convolutional Recurrent Network (C-RNet) for lung disease detection.
Utilized a publicly available dataset of CXR images for classification.
Integrated Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) with Explainable AI (XAI) techniques like Grad-CAM.

Keywords:

CNN GRAD-CAM LSTM.Lung diseases, X-ray RNN XAI

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Main Results:

The proposed C-RNet model achieved a high accuracy of 93.73% and an F1-score of 94.6%.
Outperformed traditional single-scale methods and other compared architectures in classification accuracy.
Demonstrated efficient performance with a FLOPS count of 637,222,592, 1,901,764 parameters, and a model size of 7.25MB.

Conclusions:

The C-RNet model offers a highly accurate and efficient method for diagnosing lung diseases from chest radiographs.
The integration of CNN, RNN, and XAI enhances diagnostic capabilities and allows for disease region detection.