Analysis of Emerging Preprocessing Techniques Combined with Deep CNN for Lung Disease Detection

Data preprocessing is one of the pertinent steps while classifying images via CNN models. The efficiency of any model depends on the quality of the dataset it deals with. A clean dataset provides an efficient platform for a model to tackle classification and segmentation issues. Our paper focuses on three emerging data preprocessing techniques: Real ESRGAN, Swin IR, and GFPGAN over the lung disease dataset. We have used three models: Mobile net, Densenet201, and NasNet, to carry out classification tasks on Chest X-Ray images of six different types of lung disease: Bacterial Pneumonia, Viral pneumonia, Lung opacity, Covid, Tuberculosis, and Normal. Analysis of the aforementioned preprocessing techniques followed by classification via three CNN models (Mobile net, Densenet, and NasNet) are carried out on lung disease dataset, and their accuracy prediction, Training, and validation loss are extensively compared.