Lung Tumor Detection and Localization System Using Faster RCNN Algorithm
DOI:
https://doi.org/10.65568/gujes.2026.020210الكلمات المفتاحية:
CNN، Lung tumor، Localization، Faster RCNN، ,Intersection over Unionالملخص
Lung tumors represent a significant health challenge. Although, their early detection can improve patient outcomes, traditional diagnostic methods often fall short in accuracy and efficiency. Therefore, advanced algorithms are required to facilitate accurate and earlier tumor detection and localization. This study aims to enhance detection efficacy through a systematic methodology that includes image pre-processing, segmentation, feature extraction, classification, and localization. We have invested three CNN models; Our proposed CNN, VGG-16 and RESNET50 integrated with Faster R-CNN to detect and localize lung tumor in computed tomography images from the Iraq-Oncology Teaching Hospital/National Center for Cancer Diseases (IQ-OTH/NCCD) dataset. As a result, VGG-16 and RESNET50 attained detection accuracies of 97.66% and 97.20% respectively, while Our-CNN model achieved a detection accuracy of 97.20%, with an average test precision of 97.185 and an F1-score of 0.9737. For localization, ResNet50 and Our-CNN demonstrated accuracy based on Intersection over Union (IoU) at 89.47%, while VGG-16 achieved 73.68%.
المراجع
[1] B. Rushiti, “Automatic lung cancer detection using artificial intelligence,” Bachelor’s Thesis, Prishtina, Kosovo, 2019.
[2] World Health Organization (WHO). Homepage. [Online]. Available: https://www.who.int/. Accessed: Jan. 22, 2025.
[3] World Cancer Research Fund International (WCRF). Lung cancer statistics. [Online]. Available: https://www.wcrf.org/preventing-cancer/cancer-statistics/lung-cancer-statistics/. Accessed: Feb. 15, 2025.
[4] V. R. Nitha and S. S. Vinod Chandra, “ExtRanFS: An Automated Lung Cancer Malignancy Detection System Using Extremely Randomized Feature Selector,” Diagnostics, vol. 13, no. 13, Jul. 2023, doi: 10.3390/diagnostics13132206.
[5] H. Hejbari Zargar, S. Hejbari Zargar, R. Mehri, and F. Tajdini, "Using VGG16 Algorithms for classification of lung cancer in CT scans Image," in 2023 8th International Conference on Technology and Energy Management (ICTEM), Mazandaran, Iran, 2023, pp. 1-5, doi: 10.1109/ICTEM56862.2023.10084090.
[6] A. M. A. Shaffie, "Machine learning approaches for lung cancer diagnosis," Ph.D. dissertation, Dept. of Computer Science, Univ. of Louisville, Louisville, KY, USA, 2021. doi: 10.18297/etd/3663.
[7] I. Haq et al., “Lung Nodules Localization and Report Analysis from Computerized Tomography (CT) Scan Using a Novel Machine Learning Approach,” Applied Sciences, vol. 12, no. 24, p. 12614, Dec. 2022, doi: 10.3390/app122412614.
[8] R. Pandian, V. Vedanarayanan, D. N. S. Ravi Kumar, and R. Rajakumar, “Detection and classification of lung cancer using CNN and Google net,” Measurement: Sensors, vol. 24, p. 100588, Dec. 2022, doi: 10.1016/j.measen.2022.100588.
[9] N. Nasrullah, J. Sang, M. S. Alam, and H. Xiang, “Automated detection and classification for early stage lung cancer on CT images using deep learning,” SPIE-Intl Soc Optical Eng, May 2019, p. 27. doi: 10.1117/12.2520333.
[10] C.-M. Y. Chun-Man Yan and C. W. Chun-Man Yan, “Automatic Detection and Localization of Pulmonary Nodules in CT Images Based on YOLOv5, (Journal of Computers), vol. 33, no. 3, pp. 113–123, Jun. 2022, doi: 10.53106/199115992022063303009.
[11] A. G. Akintola et al., “Integrated deep learning paradigm for comprehensive lung cancer segmentation and classification using mask R-CNN and CNN models,” Franklin Open, vol. 11, p. 100278, Jun. 2025, doi: 10.1016/j.fraope.2025.100278.
[12] K.-Y. Huang, C.-L. Chung, and J.-L. Xu, “Deep learning object detection-based early detection of lung cancer,” Front Med (Lausanne), vol. 12, Apr. 2025, doi: 10.3389/fmed.2025.1567119.
[13] Z. Li and L. Li, "A novel method for lung masses detection and location based on deep learning," in 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Kansas City, MO, USA, 2017, pp. 963-969, doi: 10.1109/BIBM.2017.8217776.
[14] H. Hamdalla, "The IQ-OTH/NCCD Lung Cancer Dataset," Kaggle, 2022. [Online]. Available: https://www.kaggle.com/datasets/hamdallak/the-iqothnccd-lung-cancer-dataset. [Accessed: Nov. 7, 2024].
[15] S. Suryawanshi and S. B. Patil, “Brain Tumor Detection Using YoloV5 and Faster RCNN,” International Journal of Intelligent Systems and Applications in Engineering, vol. 12, no. 4s, pp. 335–342,
