Transformative Approach for Prediction of Lung Disease Using Lung X-ray Images
Authors: Mehra Shrey , Singh Sunil K , Kumar Sudhakar , Dubey Harshit , Arya Varsha , Gupta Brij B. 
Published: April 2024

Abstract
Lung diseases pose a significant global health challenge, underscoring the critical need for prompt and precise diagnoses to facilitate effective treatments and enhance patient outcomes. In this research paper, we introduce an innovative method for the prediction of lung diseases by harnessing the capabilities of deep learning techniques, thereby streamlining and augmenting the diagnostic process. The Investigation commences by assembling an extensive dataset of chest Xray images sourced from diverse origins, encompassing both normal and diseased instances. Subsequently, we employ a specialized Pix2Pix Generative Adversarial Network architecture tailored for image classification. This network is meticulously trained on the comprehensive dataset, fine-tuning its abilities to discern distinctive features associated with a range of lung diseases, including pneumonia, tuberculosis, and lung cancer. The Empirical findings underscore the efficacy of the approach in diagnosing lung diseases, showcasing notable levels of accuracy, sensitivity, and specificity. Furthermore, we employ interpretability techniques to pinpoint the regions within the X-ray images that significantly contribute to the predictions, bolstering the transparency and credibility of the Model. This research presents a promising avenue for automating the diagnosis of lung diseases, with the potential to reduce human error and enhance patient care significantly. It holds the promise of aiding in early detection and intervention, potentially saving lives and alleviating the burden of lung diseases on healthcare systems worldwide. The integration of DenseNet architecture into our predictive model significantly enhances the accuracy and efficiency of diagnosing lung diseases from X-ray images. DenseNet’s interconnected layers facilitate collaborative learning, enabling the model to discern intricate details crucial for precise classifications. The adoption of DenseNet not only amplifies diagnostic precision but also paves the way for future strides in medical image analysis, particularly in advancing respiratory health diagnostics. Along with this under the investigation comprising the chest X-ray images in JPEG format, it is systematically categorized into train, test, and val directories, each further subdivided into folders representing Pneumonia and Normal classes. These images depict anterior-posterior chest radiographs obtained from pediatric patients aged one to five years at the Guangzhou Women and Children’s Medical Center, forming an integral part of routine clinical care for this specific demographic. To ensure the dataset’s integrity, a rigorous initial screening process is applied to exclude any low-quality or unreadable scans. Subsequently, two expert physicians meticulously evaluate and grade the diagnostic quality of the remaining images. Only those that successfully pass this comprehensive scrutiny are deemed suitable for the subsequent training of the artificial intelligence system. This meticulous curation underscores the reliability and high quality of the dataset, emphasizing its potential for advancing medical image analysis within the context of pediatric chest radiographs.