Document Type : Technical Paper

Authors

• Mohammad Mahdi Nakhaie ¹
• Sasan Karamizadeh ^{1, 2}
• Mohammad Ebrahim Shiri ³
• Kambiz Badie ⁴

¹ Ershad Damavand Institute of Higher Education, Tehran, Iran.

² ICT Research Institute, Tehran, Iran.

³ Amirkabir University of Technology, Tehran, Iran.

⁴ E-Content & E-Services Research Group, IT Research Faculty, ICT Research Institute, Karegar, Tehran, 14155-3961, Tehran, Iran.

Abstract

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 LUNA16, achieving impressive accuracies of 99.7% and 97.52%, respectively. These results demonstrate the superior accuracy of our proposed model compared to state-of-the-art research papers.

Keywords

• Lung cancer
• Deep learning
• LIDC-IDRI
• LUNA16
• Rotated

Main Subjects

• H.5. Image Processing and Computer Vision