LungNet: A hybrid deep-CNN model for lung cancer diagnosis using CT and wearable sensor-based medical IoT data. (December 2021) - Faruqui, Nuruzzaman; Yousuf, Mohammad Abu; Whaiduzzaman, Md; Azad, A.K.M.; Barros, Alistair; Moni, Mohammad Ali
Abstract: Lung cancer, also known as pulmonary cancer, is one of the deadliest cancers, but yet curable if detected at the early stage. At present, the ambiguous features of the lung cancer nodule make the computer-aided automatic diagnosis a challenging task. To alleviate this, we present LungNet, a novel hybrid deep-convolutional neural network-based model, trained with CT scan and wearable sensor-based medical IoT (MIoT) data. LungNet consists of a unique 22-layers Convolutional Neural Network (CNN), which combines latent features that are learned from CT scan images and MIoT data to enhance the diagnostic accuracy of the system. Operated from a centralized server, the network has been trained with a balanced dataset having 525, 000 images that can classify lung cancer into five classes with high accuracy (96.81%) and low false positive rate (3.35%), outperforming similar CNN-based classifiers. Moreover, it classifies the stage-1 and stage-2 lung cancers into 1A, 1B, 2A and 2B sub-classes with 91.6% accuracy and false positive rate of 7.25%. High predictive capability accompanied with sub-stage classification renders LungNet as a promising prospect in developing CNN-based automatic lung cancer diagnosis systems. Highlights: Reliable CNN-based automatic diagnosis with global access helps early diagnosis of Lung Cancer. Both CT images and physiological information are good indicators of the level of malignancy of lung cancer. A hybrid classifier considers image features and symptomatic data in lung cancer classification. A trained deep Convolutional Neural Network (CNN) with a hybrid classifier operated from a centralized server allows ubiquitous access with reliable diagnosis service.