Automated Lung Nodule Detection and Classification Using Deep Learning Combined with Multiple Strategies

• Published in: Sensors (Basel, Switzerland) Vol. 19; no. 17; p. 3722
• Main Authors: Nasrullah, Nasrullah, Sang, Jun, Alam, Mohammad S, Mateen, Muhammad, Cai, Bin, Hu, Haibo
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 28.08.2019; MDPI
• Subjects: Automation; Biomarkers; Body area networks; Classification; clinical biomarkers; Computed tomography; Databases, Factual; deep convolutional neural networks; Deep Learning; Diagnosis; Diagnosis, Computer-Assisted; Early Detection of Cancer; Humans; Image analysis; Image databases; Image Processing, Computer-Assisted - methods; Internet of Things; Localization; Lung - diagnostic imaging; Lung - physiology; Lung cancer; Lung Neoplasms - diagnosis; Lung Neoplasms - diagnostic imaging; Lung Neoplasms - pathology; Medical imaging; Medical prognosis; Morphology; Neoplasms - diagnosis; Neoplasms - diagnostic imaging; Neoplasms - pathology; Neural networks; Neural Networks, Computer; Nodules; Pattern recognition; Physiology; pulmonary nodules; Radiographic Image Interpretation, Computer-Assisted - methods; Radiographs; Sensitivity analysis; Signs and symptoms; Software engineering; Tomography; Tomography, X-Ray Computed; wireless body area networks; Wireless networks; Wireless Technology; Workloads; Pakistan; Islamabad Pakistan; United States > US; China; pulmonary nodules; wireless body area networks; deep convolutional neural networks; internet of things; clinical biomarkers

Lung cancer is one of the major causes of cancer-related deaths due to its aggressive nature and delayed detections at advanced stages. Early detection of lung cancer is very important for the survival of an individual, and is a significant challenging problem. Generally, chest radiographs (X-ray) a...