Enhanced Detection and Classification of Chest Diseases Using Modified VGG16 Deep Neural Network

Abstract

For efficient medical diagnostics and patient care, prompt and accurate identification of chest disorders is required. Conventional techniques that depend on manual radiography picture interpretation are frequently hampered by time restrictions and human error. In order to improve the diagnosis of chest ailments, this research investigates the integration of deep learning techniques, particularly deep convolutional neural networks (CNNs). We investigate the possibility of VGG16, a well-known CNN architecture created by the Visual Geometry Group (VGG), to automate and enhance diagnostic accuracy. VGG16, characterized by its 16-layer architecture and use of small receptive fields, excels in recognizing complex patterns within medical images. Its application in detecting conditions such as atelectasis, cardiomegaly, masses, and nodules is discussed, highlighting its ability to discern subtle abnormalities and facilitate early diagnosis. Through a review of recent advancements and performance evaluations on benchmark datasets, this paper provides in- sights into VGG16’s effectiveness, identifies key challenges, and outlines future research directions in lever- aging deep learning for medical imaging.