High-precision multiclass classification of lung disease through customized MobileNetV2 from chest X-ray images. (March 2023)

• Record Type: Journal Article
• Authors: Shamrat, FM Javed Mehedi; Azam, Sami; Karim, Asif; Ahmed, Kawsar; Bui, Francis M.; De Boer, Friso
• Is Part Of: Computers in biology and medicine. Volume 155(2023)
• Publication Date: 2023-03
• Subjects: MobileLungNetV2 -- MobileNetV2 -- CNN -- Lung disease -- Deep learning -- Multiclass classification; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2023.106646
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: In this study, multiple lung diseases are diagnosed with the help of the Neural Network algorithm. Specifically, Emphysema, Infiltration, Mass, Pleural Thickening, Pneumonia, Pneumothorax, Atelectasis, Edema, Effusion, Hernia, Cardiomegaly, Pulmonary Fibrosis, Nodule, and Consolidation, are studied from the ChestX-ray14 dataset. A proposed fine-tuned MobileLungNetV2 model is employed for analysis. Initially, pre-processing is done on the X-ray images from the dataset using CLAHE to increase image contrast. Additionally, a Gaussian Filter, to denoise images, and data augmentation methods are used. The pre-processed images are fed into several transfer learning models; such as InceptionV3, AlexNet, DenseNet121, VGG19, and MobileNetV2. Among these models, MobileNetV2 performed with the highest accuracy of 91.6% in overall classifying lesions on Chest X-ray Images. This model is then fine-tuned to optimise the MobileLungNetV2 model. On the pre-processed data, the fine-tuned model, MobileLungNetV2, achieves an extraordinary classification accuracy of 96.97%. Using a confusion matrix for all the classes, it is determined that the model has an overall high precision, recall, and specificity scores of 96.71%, 96.83% and 99.78% respectively. The study employs the Grad-cam output to determine the heatmap of disease detection. The proposed model shows promising results in classifying multiple lesions on Chest X-ray images. Highlights: This paper proposes a MobileNetV2-based fine-tuned model. The highlights are: To classify lung diseases from X-ray image data. The proposed model classifies data with higher accuracy than pre-trained models. The model performs image feature extraction while identifying abnormalities. Literature comparison shows the model outperforms existing studies.