A Hybrid Approach using CNN and DQN Technique for Diagnosis Pneumonia in Chest X-Ray Images

Abstract

Pneumonia poses a significant risk to life and well-being respiratory infection that requires accurate and timely diagnosis for effective treatment. In this research investigation, it proposes a hybrid approach for detecting pneumonia diagnosis in chest X-ray images by combining machine learning techniques with convolutional neural networks (CNN), and deep Q-network (DQN) reinforcement learning. The suggested approach holds promising prospects for enhancing the efficacy of pneumonia diagnosis. Especially in resource-limited settings where access to radiologists or specialized equipment is limited. The proposed hybrid approach involves multiple stages. Initially, an extensive collection dataset of chest X-ray images, comprising both normal and pneumonia cases, is collected. The CNN model can be integrated into clinical decision support systems to provide accurate diagnosis of infection for pneumonia. Furthermore, the use of the rainbow method can be extended to other clinical imaging tasks to enhance deep learning models performance Additionally, it demonstrates that the use of the rainbow method improves the performance of the CNN, leading to a higher accuracy. We have introduced a novel hybrid deep learning framework called LIP-CDF Algorithm, which combines algorithms of Convolutional Neural Networks (CNN) and Deep Q-Network (DQN) techniques. LIP-CDF (Lung Infection Prediction using CNN-DQN Fusion) Algorithm is a computational approach designed for the accurate and efficient lung infection prediction using images from chest X-rays. The implementation of this framework utilized popular tools such as Jupyter Notebook, TensorFlow, and Keras. To assess the effectiveness of our model, the NIH chest X-beam picture dataset gained from the Kaggle archive. To evaluate the effectiveness of our proposed approach, we conduct experiments on the publicly available Chest X-ray14 dataset. The results show that our approach achieves a high accuracy of 94.8% in detecting pneumonia cases.
 The purpose of our framework is to streamline the detection of lung diseases, making it easier for both medical experts and doctors. By harnessing the power of CNN and DQN, our approach offers a simplified yet accurate method for identifying lung diseases from chest X-ray images. This advancement in deep learning technology has the potential to greatly assist healthcare professionals in diagnosing and treating patients effectively