Iranian Journal of Biomedical Engineering (IJBME)
نشریه علمی مهندسی پزشکی زیستی

Lung Cancer Diagnosis from CT Scan Images using Deep Learning and Transfer Learning Methods

Document Type : Full Research Paper

Authors

Kambiz Rahbar 1
Fatemeh Taheri 2

1 Assistant Professor, Department of Computer Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran

2 Ph.D. Candidate, Department of Computer Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran

https://doi.org/10.22041/ijbme.2024.2015895.1870
Abstract
Lung cancer is caused by the irregular and uncontrolled growth of cancer cells in the lung tissue. Cancer cells find the ability to divide and increase in an irregular and uncoordinated manner. The result of this proliferation is the formation of a cancerous mass in the lung. Lung cancer can start in different parts of the lung, such as the bronchi (the air tubes that connect to the lungs) or non-bronchial tissues, and quickly spread to other parts of the body. The precise understanding of the mechanism of lung cancer is still a complex issue and many researches are being conducted in this field. However, early diagnosis has an important impact on the disease treatment process. Therefore, in this research, the diagnosis and classification of this disease is discussed with the help of deep learning and transfer learning. In this regard, the pre-trained Alexnet network has been selected. During the process of transfer learning, the network for lung cancer detection is set on IQ-OTH/NCCD data in three categories: normal, benign and malignant. For this purpose, the last all-connection layer of the Alexnet network is removed and replaced by a new all-connection layer corresponding to the number of layers in the dataset. The classification accuracy of the proposed method on the IQ-OTH/NCCD dataset is reported to be 93%.

Keywords

Lung Cancer
CT Images
Deep Learning
Transfer Learning

Subjects

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Iranian Journal of Biomedical Engineering (IJBME)
Volume 17, Issue 2
Summer 2023
Pages 165-177

  • Receive Date 16 November 2023
  • Revise Date 02 February 2024
  • Accept Date 20 February 2024

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