Determination of Location and Severity of Nodules on Lung Cancer CT Image Using YOLO Methods

Hanun Masitha Ramadhani, Chastine Fatichah

Abstract


The severity of lung cancer can be used to determine appropriate treatment measures and reduce the risk of death. The severity identification is monitored based on the size and location of the nodule. However, previous studies still focused on determining the location of nodules without identifying their severity. In this study, the severity of lung cancer is detected based on the size of its nodules. This research contributes to the annotation of severity to the Lung Image Database Consortium image collection (LIDC-IDRI) dataset and the development of automatic severity detection using You Only Look Once (YOLO) methods. The data is given a severity level based on the nodule size calculated based on the number of pixels in the nodule length. Automatic detection is done using YOLO methods, which consist of several versions, namely YOLOv5, YOLOv7, and YOLOv8. YOLO methods can properly detect the location and severity of cancer nodules with the IoU evaluation results obtained using YOLOv5, YOLOv7, and YOLOv8, which are 0.86, 0.6, and 0.87, respectively. From the experiment, it can be concluded that determining the location and severity of cancer based on nodule size using YOLO methods is proven effective and can be done in real-time.

Keywords


Lung Cancer; Nodule’s Severity; Object Detection; YOLO

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References


Joseph J, Rotty LWA. Kanker Paru: Laporan Kasus. Medical Scope Journal 2020;2.

Cao W, Chen HD, Yu YW, Li N, Chen WQ. Changing profiles of cancer burden worldwide and in China: A secondary analysis of the global cancer statistics 2020. Chinese Medical Journal 2021;134.

Ramadhaniah F, Syarif S. Studi Tinjauan Pustaka: Risiko Kejadian Kanker Paru pada Penderita Tuberkulosis Paru. Jurnal Epidemiologi Kesehatan Indonesia 2020;4.

Gregson CL, Armstrong DJ, Bowden J, Cooper C, Edwards J, Gittoes NJL, et al. UK clinical guideline for the prevention and treatment of osteoporosis. Archives of Osteoporosis 2022;17.

Larici AR, Farchione A, Franchi P, Ciliberto M, Cicchetti G, Calandriello L, et al. Lung nodules: Size still matters. European Respiratory Review 2017;26.

Vaghasiya K, Sharma A, Verma RK. Misdiagnosis Murder: Disguised TB or Lung Cancer? Pulmonary Research and Respiratory Medicine - Open Journal 2016 9;3:e5–e6.

Ciello Ad, Franchi P, Contegiacomo A, Cicchetti G, Bonomo L, Larici AR. Missed lung cancer: When, where, and why? Diagnostic and Interventional Radiology 2017;23.

Roth HR, Lu L, Liu J, Yao J, Seff A, Cherry K, et al. Improving Computer-Aided Detection Using Convolutional Neural Networks and Random View Aggregation. IEEE Transactions on Medical Imaging 2016;35(5):1170–1181.

Liu C, Hu SC, Wang C, Lafata K, Yin FF. Automatic detection of pulmonary nodules on CT images with YOLOv3: development and evaluation using simulated and patient data. Quantitative Imaging in Medicine and Surgery 2020;10.

Wang J, Wang J, Wen Y, Lu H, Niu T, Pan J, et al. Pulmonary Nodule Detection in Volumetric Chest CT Scans Using CNNs-Based Nodule-Size-Adaptive Detection and Classification. IEEE Access 2019;7.

Wang Z, Liu H, Zhang G, Yang X, Wen L, Zhao W. Diseased Fish Detection in the Underwater Environment Using an Improved YOLOV5 Network for Intensive Aquaculture. Fishes 2023;8.

Armato SG, McLennan G, Bidaut L, McNitt-Gray MF, Meyer CR, Reeves AP, et al. The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): A completed reference database of lung nodules on CT scans. Medical Physics 2011;38.

Horeweg N, van Rosmalen J, Heuvelmans MA, van der Aalst CM, Vliegenthart R, Scholten ET, et al. Lung cancer probability in patients with CT-detected pulmonary nodules: A prespecified analysis of data from the NELSON trial of low-dose CT screening. The Lancet Oncology 2014;15.

MacMahon H, Naidich DP, Goo JM, Lee KS, Leung ANC, Mayo JR, et al. Guidelines for management of incidental pulmonary nodules detected on CT images: From the Fleischner Society 2017. Radiology 2017;284.

Buslaev A, Iglovikov VI, Khvedchenya E, Parinov A, Druzhinin M, Kalinin AA. Albumentations: Fast and flexible image augmentations. Information (Switzerland) 2020;11.

Geraldy C, Lubis C. Detecting and Identifying Vehiche Type Using YOLO and CNN (Pendeteksian dan pengenalan jenis mobil menggunakan algoritma You Only Look Once dan Convolutional Neural Network. Jurnal Ilmu Komputer dan Sistem Informasi 2020;8.

Jupiyandi S, Saniputra FR, Pratama Y, Dharmawan MR, Cholissodin I. Development of vehicle detection for counting utility in parking space using CUDA and Modified-YOLO (Pengembangan Deteksi Citra Mobil untuk Mengetahui Jumlah Tempat Parkir Menggunakan CUDA dan Modified YOLO). Jurnal Teknologi Informasi dan Ilmu Komputer 2019;6.

Jiang P, Ergu D, Liu F, Cai Y, Ma B. A Review of Yolo Algorithm Developments. In: Procedia Computer Science, vol. 199; 2021. p. 733–736.

Chen S, Duan J,Wang H,Wang R, Li J, Qi M, et al. Automatic detection of stroke lesion from diffusion-weighted imaging via the improved YOLOv5. Computers in Biology and Medicine 2022;150.

Wang CY, Bochkovskiy A, Liao HYM. YOLOv7: Trainable Bag-of-Freebies Sets New State-of-the-Art for Real-Time Object Detectors. In: 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023. p. 7464–7475.

Qadri SAA, Huang NF, Wani TM, Bhat SA. Plant Disease Detection and Segmentation using End-to-End YOLOv8: A Comprehensive Approach. In: 2023 IEEE 13th International Conference on Control System, Computing and Engineering (ICCSCE); 2023. p. 155–160.

Wang X, Gao H, Jia Z, Li Z. BL-YOLOv8: An Improved Road Defect Detection Model Based on YOLOv8. Sensors (Basel, Switzerland) 2023;23.

Mishra S, Dash A, Jena L. 10. In: Bhoi AK, Mallick PK, Liu CM, Balas VE, editors. Use of Deep Learning for Disease Detection and Diagnosis Singapore: Springer Singapore; 2021. p. 181–201.

Rachh R, Allagi S, Shravan BK. 11. In: N P, Kautish S, Peng SL, editors. Chapter 11 - Machine learning algorithms for prediction of heart disease Academic Press; 2021. p. 247–275. https://www.sciencedirect.com/science/article/pii/B9780128216330000106.




DOI: http://dx.doi.org/10.12962/j20882033.v34i2.16821

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