Saabia, Abd Al-Baset, Azawi, Raghad, Kadhim, Rasha. (1401). Classification of lung cancer histology using CT images based on convolutional neural network-CNN. هنرهای کاربردی, 13(2), 3281-3289. doi: 10.22075/ijnaa.2022.6847
Abd Al-Baset Rashed Saabia; Raghad Majeed Azawi; Rasha Amer Kadhim. "Classification of lung cancer histology using CT images based on convolutional neural network-CNN". هنرهای کاربردی, 13, 2, 1401, 3281-3289. doi: 10.22075/ijnaa.2022.6847
Saabia, Abd Al-Baset, Azawi, Raghad, Kadhim, Rasha. (1401). 'Classification of lung cancer histology using CT images based on convolutional neural network-CNN', هنرهای کاربردی, 13(2), pp. 3281-3289. doi: 10.22075/ijnaa.2022.6847
Saabia, Abd Al-Baset, Azawi, Raghad, Kadhim, Rasha. Classification of lung cancer histology using CT images based on convolutional neural network-CNN. هنرهای کاربردی, 1401; 13(2): 3281-3289. doi: 10.22075/ijnaa.2022.6847

Classification of lung cancer histology using CT images based on convolutional neural network-CNN

International Journal of Nonlinear Analysis and Applications
دوره 13، شماره 2، مهر 2022، صفحه 3281-3289    اصل مقاله (834.81 K)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22075/ijnaa.2022.6847
نویسندگان
Abd Al-Baset Rashed Saabia* 1؛ Raghad Majeed Azawi2؛ Rasha Amer Kadhim3
1Ministry of Education, The General Directorate for Education of Diyala, Iraq
2Computer of Science, College of Medicine, Diyala University, Diyala, Iraq
3College of Agriculture, University of Diyala, Diyala, Iraq
چکیده
In the field of medical imaging, interest in deep learning as a talented new discipline and a key component is growing rapidly. In lung cancer, the histology of tumours is a key predictor of therapy response and outcome. The most accurate way to classify histologies is through a pathologist's examination of tissue samples. Early tumour detection and therapy are essential for patients' recovery. For the purpose of identifying illness signs and risk stratification, recent developments in machine learning, medical image processing, and the use of radiologic data are highlighted. Regarding lung cancer and its various subtypes, it is error-prone and time-consuming to identify and differentiate. Convolutional Neural Networks can more accurately and quickly diagnose different types of lung cancer, which is essential for choosing the best treatment plan and patient survival rate. This research is concerned with both malignant (tumour) and benign tissue cell carcinomas. A training accuracy of 98.60 percent is obtained for the classification system of the suggested system, which is superior to that of both classic neural networks and the CNN model.
کلیدواژه‌ها
Deep Learning؛ Tumors؛ Classification؛ Convolutional Neural Network؛ Accuracy
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