Using Convolutional Neural Network to Enhance Classification Accuracy of Cancerous Lung Masses from CT Scan Images | ||
| Journal of AI and Data Mining | ||
| دوره 11، شماره 4، بهمن 2023، صفحه 547-559 اصل مقاله (1.33 M) | ||
| نوع مقاله: Technical Paper | ||
| شناسه دیجیتال (DOI): 10.22044/jadm.2023.13670.2486 | ||
| نویسندگان | ||
| Mohammad Mahdi Nakhaie1؛ Sasan Karamizadeh* 1، 2؛ Mohammad Ebrahim Shiri3؛ Kambiz Badie4 | ||
| 1Ershad Damavand Institute of Higher Education, Tehran, Iran. | ||
| 2ICT Research Institute, Tehran, Iran. | ||
| 3Amirkabir University of Technology, Tehran, Iran. | ||
| 4E-Content & E-Services Research Group, IT Research Faculty, ICT Research Institute, Karegar, Tehran, 14155-3961, 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 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. | ||
| کلیدواژهها | ||
| Lung cancer؛ Deep learning؛ LIDC-IDRI؛ LUNA16؛ Rotated | ||
| مراجع | ||
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