Implementing Advanced Hybrid Algorithms for Detecting Infectious Diseases in Chest X-Rays

In this paper, the development of hybrid algorithms for the detection of Pneumonia Disease from the human X-Ray’s images of the Chest Structure is presented along with the simulated & experimental results. w.r.t. the background scenario, Pneumonia, a potentially fatal lung infection caused by various viral infections, poses diagnostic challenges due to its similarities with other lung disorders when analyzing chest X-ray images. Early and accurate detection through chest X-rays is vital for prompt intervention, complication prevention, and reduced hospital stays. w.r.t the methods employed, this paper presents a novel approach utilizing hybrid algorithms for pneumonia detection from chest X-ray images, combining convolutional neural networks (CNN), VGG16, ResNet152V2, and Gradient Descent optimization. The method involves preprocessing X-ray images to enhance features, followed by feature extraction using both traditional machine learning and deep learning models. Pre-trained models ResNet152V2 and VGG16 are integrated to improve overall system performance. w.r.t. the results, the system automatically extracts features from chest X-ray images using CNNs, enhanced by Gradient Descent optimization to better discriminate between pneumonia patients and healthy cases. Experimental results indicate that the proposed hybrid algorithms significantly outperform traditional approaches, achieving high accuracy in pneumonia detection. Concluding remarks says the study demonstrates the potential of deep learning and hybrid algorithms for more precise and efficient pneumonia diagnosis in clinical settings, highlighting their role in enhancing early and effective medical interventions. These results highlight the potential of hybrid algorithms for improving the diagnosis of pneumonia from chest’s X-ray’s images, thereby aiding in early and effective medical intervention.