Implementation of Neural Network Algorithm (RNN) for Lung Disease Classification

This research examines the classification of lung diseases using the Recurrent Neural Network algorithm, based on medical images, specifically X-ray images.The method aims to improve the accuracy of lung disease diagnosis by leveraging the capabilities of Recurrent Neural Networks in recognizing complex patterns in sequential data.RNNs are particularly suited for this task due to their ability to process sequences of data, making them effective in analyzing time-dependent or ordered information.In this study, an RNN model has been developed and evaluated using a dataset of lung X-ray images sourced from reputable medical databases.The dataset encompasses various lung conditions, including pneumonia, tuberculosis, and lung cancer, allowing for comprehensive model training.The results show that the RNN model achieves an accuracy of 62.9 percent, indicating its potential in supporting automated medical diagnostic processes.While this accuracy is promising, it also highlights the need for further development and fine-tuning of the model to enhance performance.By improving the accuracy of automated diagnoses, this research contributes significantly to the advancement of decision support systems for clinical practice in the medical field, ultimately aiding healthcare professionals in delivering timely and accurate diagnoses for lung diseases.