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.

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.

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