Using Convolutional Neural Network to Enhance Classification Accuracy of Cancerous Lung Masses from CT Scan Images

  • سال انتشار: 1402
  • محل انتشار: مجله هوش مصنوعی و داده کاوی، دوره: 11، شماره: 4
  • کد COI اختصاصی: JR_JADM-11-4_005
  • زبان مقاله: انگلیسی
  • تعداد مشاهده: 203
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نویسندگان

Mohammad Mahdi Nakhaie

Ershad Damavand Institute of Higher Education, Tehran, Iran.

Sasan Karamizadeh

Ershad Damavand Institute of Higher Education, Tehran, Iran.

Mohammad Ebrahim Shiri

Amirkabir University of Technology, Tehran, Iran.

Kambiz Badie

E-Content & E-Services Research Group, IT Research Faculty, ICT Research Institute, Karegar, Tehran, ۱۴۱۵۵-۳۹۶۱, Tehran, Iran.

چکیده

Lung cancer is a highly serious illness, and detecting cancer cells early significantly enhances patients' chances of recovery. Doctors regularly examine a large number of CT scan images, which can lead to fatigue and errors. Therefore, there is a need to create a tool that can automatically detect and classify lung nodules in their early stages. Computer-aided diagnosis systems, often employing image processing and machine learning techniques, assist radiologists in identifying and categorizing these nodules. Previous studies have often used complex models or pre-trained networks that demand significant computational power and a long time to execute. Our goal is to achieve accurate diagnosis without the need for extensive computational resources. We introduce a simple convolutional neural network with only two convolution layers, capable of accurately classifying nodules without requiring advanced computing capabilities. We conducted training and validation on two datasets, LIDC-IDRI and LUNA۱۶, achieving impressive accuracies of ۹۹.۷% and ۹۷.۵۲%, respectively. These results demonstrate the superior accuracy of our proposed model compared to state-of-the-art research papers.

کلیدواژه ها

Lung cancer, deep learning, LIDC-IDRI, LUNA۱۶, Rotated

اطلاعات بیشتر در مورد COI

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