Histopathological Image Analysis for Lung Disease Diagnosis: A VGG16-Based Approach

This paper presents a VGG-16-based predictive model for the diagnosis of lung diseases using histopathological samples. Early and accurate detection through automated systems has become very essential in view of global health challenges caused by lung diseases. For training and testing the model, the researchers used the LC2500 dataset that contains different images of lung tissues. To enhance the model's performance, some preprocessing techniques were applied to the images, such as resizing, normalization, and augmentation. In this work, the VGG-16 model was fine-tuned, which proved efficient in solving image classification tasks. The accuracy achieved during the experiment was 92%, with high precision and recall and corresponding Fl scores, thus proving that this model does excellently in differentiating various conditions of the lungs. Results show evidence regarding the potential of deep learning methodologies in the analysis of medical images to help improve diagnostic accuracy and hence the condition of patients suffering from respiratory diseases.