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ARTIFICIAL INTELLIGENCE BASED LUNG CANCER DETECTION USING DEEP LEARNING TECHNIQUES

  • Unique Paper ID: 158798
  • Volume: 9
  • Issue: 10
  • PageNo: 748-754
  • Abstract:
  • The majority of deaths in both men and women are caused by lung cancer, one of the deadliest cancers in the nation. 10% to 20% of lung cancer patients only have a five-year survival rate. The death rate can be reduced, though, if lung cancer is found early and treated right away. It is a highly challenging assignment for radiologists, but many nations have developed screening programmes to promote early identification of lung cancer from Computed Tomography (CT) pictures. It takes a lot of time to manually identify malignant lung nodules from these CT pictures. The clinical response to therapy may differ and can have a very favourable response when lung cancer is discovered early during the screening phase. A trustworthy, automated method may be very beneficial in both rural and early-stage lung cancer identification. A recent development in medical image analysis, particularly for lung cancer, is deep learning. The focus of the work that has been presented is on fusing clinical practises with deep learning algorithms to help radiologists diagnose pulmonary nodules and eliminate most false-negative images in lung cancer screening. The availability of a vast amount of data for training and testing is the primary factor contributing to the higher success rate of deep learning algorithms in medical imaging. A variety of datasets are available for the detection of lung cancer nodules, enabling deep learning to boost screening operations' efficiency and ultimately contributing to a decrease in lung cancer mortality and benefiting individuals in remote areas. Numerous studies have been done on identifying and classifying lung cancer, but the precision needed to apply such models for clinical applications and for radiologists to diagnose the carcinoma remains a significant barrier. The goal of transfer learning is to transfer information from one domain to another. It is a deep learning approach where characteristics from the source problem are applied to a different but related target problem. For achieving better accuracy, the research work presented functioning of pre-trained models MobileNet, Xception and VGG-16 for finding of lung cancer nodules from lung CT images. The dataset was collected from the LIDC-IDRI da
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Cite This Article

  • ISSN: 2349-6002
  • Volume: 9
  • Issue: 10
  • PageNo: 748-754

ARTIFICIAL INTELLIGENCE BASED LUNG CANCER DETECTION USING DEEP LEARNING TECHNIQUES

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