LungNet: A hybrid deep-CNN model for lung cancer diagnosis using CT and wearable sensor-based medical IoT data

Nuruzzaman Faruqui, Mohammad Abu Yousuf, Md Whaiduzzaman, A. K.M. Azad, Alistair Barros, Mohammad Ali Moni

Research output: Contribution to journalArticlepeer-review

78 Citations (Scopus)

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.

Original languageEnglish
Article number104961
JournalComputers in Biology and Medicine
Volume139
DOIs
Publication statusPublished - Dec 2021

Keywords

  • Centralized server
  • CNN Architecture
  • Feature enhancement
  • LungNet
  • Medical internet of things
  • Stage classification

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