AUTHOR=Tandon Ritu , Agrawal Shweta , Chang Arthur , Band Shahab S. 

TITLE=VCNet: Hybrid Deep Learning Model for Detection and Classification of Lung Carcinoma Using Chest Radiographs

JOURNAL=Frontiers in Public Health

VOLUME=Volume 10 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2022.894920

DOI=10.3389/fpubh.2022.894920

ISSN=2296-2565

ABSTRACT=Detection of malignant lung nodules from Computed Tomography (CT) images is a significant task for radiologists. But, it is time-consuming in nature. Despite numerous breakthroughs in studies on the application of deep learning models for the identification of lung cancer, researchers and doctors still face challenges when trying to deploy the model in clinical settings to achieve improved accuracy and sensitivity on huge datasets. In most situations, deep convolutional neural networks are used for detecting the region of the main nodule of the lung exclusive of considering the neighboring tissues of the nodule. In this paper, a hybrid model named as VCNet is proposed by combining the features of VGG-16 and capsule network (CapsNet).VGG-16 model is used for object recognition and classification. CapsNet is used to address the shortcomings of convolutional neural networks for image rotation, tiling, and other abnormal image orientations. The performance of VCNET is verified on the Lung Image Database Consortium (LIDC) image collection dataset. It achieves higher testing accuracy of 99.49% which is significantly better than MobileNet, Xception, and VGG-16 that has achieved an accuracy of 98%, 97.97%, and 96.95%, respectively. Therefore, the proposed hybrid VCNet framework can be used for the clinical purpose for nodule detection in lung carcinoma detection.