Lung Cancer classification using an ensemble of CNNs Method in CT Scan Images

المؤلفون

  • Ghufran Abbas Betti Electrical and Computer Engineering Department, University of Tabriz, Iran
  • Ali H. Naser Ministry Of Water Recourses , Iraq
  • Dr. Jafar Tanha Electrical and Computer Engineering Department, University of Tabriz, Iran
  • Saeed Bashazadeh Bashazadeh Electrical and Computer Engineering Department, University of Tabriz, Iran

DOI:

https://doi.org/10.32792/jeps.v14i2.435

الكلمات المفتاحية:

Deep learning, CNN, Lung cancer

الملخص

About five million people lose their lives every year to lung cancer, making it one of the leading causes of mortality worldwide. In the last few years, a lot of methods of detection of lung cancer were improved however these could not efficiently diagnose cancer. In this paper, a convolutional neural network (CNN) of robust deep learning is developed. CNN precision raises the deeper that is, however, it causes over-fitting or vanishing gradient problems simultaneously. To solve the issue, the CNN used resort to parallel CNN. It used Pictures the LIDC-IDRI consortium image collection has thoracic CT images that have been annotated for lung cancer diagnosis and screening purposes. The presented model includes result models of CNN which are integrated via ensemble methods and it compare with every model. Results of the simulation illustrate that the presented method's accuracy has developed by 2.18 percent in comparison  with the method of the main paper

التنزيلات

منشور

2024-06-01

إصدار

مجلد 14 عدد 2 (2024): JOURNAL OF EDUCATION FOR PURE SCIENCE- UNIVERSITY OF THI-QAR

القسم

Articles

الرخصة

الحقوق الفكرية (c) 2024 Journal of Education for Pure Science- University of Thi-Qar

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