Deep Neural Network for Lung Image Segmentation on Chest X-ray

• Published in: Technologies (Basel) 2022-09, Vol.10 (5), p.105
• Main Authors: Chavan, Mahesh, Varadarajan, Vijayakumar, Gite, Shilpa, Kotecha, Ketan
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Analysis; Artificial intelligence; Artificial neural networks; CNN; Comparative analysis; Computed tomography; Coronaviruses; COVID-19; Datasets; deep neural networks; Disease transmission; Feature extraction; Identification and classification; Image segmentation; lung image; Lungs; Medical imaging; Medical research; Methods; Morphology; nCoV; Neural networks; Scientific papers; segmentation; Semantics; Severe acute respiratory syndrome coronavirus 2; Tomography; X-rays
• ISSN: 2227-7080; 2227-7080
• DOI: 10.3390/technologies10050105

COVID-19 patients require effective diagnostic methods, which are currently in short supply. In this study, we explained how to accurately identify the lung regions on the X-ray scans of such people’s lungs. Images from X-rays or CT scans are critical in the healthcare business. Image data categorization and segmentation algorithms have been developed to help doctors save time and reduce manual errors during the diagnosis. Over time, CNNs have consistently outperformed other image segmentation algorithms. Various architectures are presently based on CNNs such as ResNet, U-Net, VGG-16, etc. This paper merged the U-Net image segmentation and ResNet feature extraction networks to construct the ResUNet++ network. The paper’s novelty lies in the detailed discussion and implementation of the ResUNet++ architecture in lung image segmentation. In this research paper, we compared the ResUNet++ architecture with two other popular segmentation architectures. The ResNet residual block helps us in lowering the feature reduction issues. ResUNet++ performed well compared with the UNet and ResNet architectures by achieving high evaluation scores with the validation dice coefficient (96.36%), validation mean IoU (94.17%), and validation binary accuracy (98.07%). The novelty of this research paper lies in a detailed discussion of the UNet and ResUNet architectures and the implementation of ResUNet++ in lung images. As per our knowledge, until now, the ResUNet++ architecture has not been performed on lung image segmentation. We ran both the UNet and ResNet models for the same amount of epochs and found that the ResUNet++ architecture achieved higher accuracy with fewer epochs. In addition, the ResUNet model gave us higher accuracy (94%) than the UNet model (92%).