Kompyuter rentgen tasvirlarini simmetrik qoʻshimcha ikki chiziqli tashxislash algoritmi
Боковая панель статьи
Основное содержимое статьи
Аннотация
COVID-19 epidemiyasi dunyoning barcha burchaklariga tarqaldi, natijada son-sanoqsiz infektsiyalar va oʻlimlar kuzatildi. Ushbu tadqiqotda uchta asosiy modulga boʻlingan zaif nazorat qilinadigan simmetrik qoʻshimcha ikki chiziqli tasniflash tarmogʻi taklif etiladi: optimallashtirishni qidiradigan zaif nazorat qilinadigan segmentatsiyani oldindan ishlov berish moduli (O-WSSPM), nosimmetrik qoʻshimcha ikki chiziqli modul (S-CBM) va FCM. Klaster vizualizatsiya moduli (FCMM). Birinchi modul, O-WSSPM, yangi Data1-Seg ma’lumotlar toʻplamini yaratish uchun KT tasvirlaridan ortiqcha fon xususiyatlarini olib tashlaydi, bu asosan asosiy xususiyat sohalarini saqlashga xizmat qiladi. Ikkinchi modul S-CBM asosan turli xil xususiyatlarni ajratib olish va shu bilan boy qoʻshimcha funktsiyalarni olish uchun nosimmetrik ikkita tarmoqdan foydalanadi. Uchinchi modul FCMM yorliqsiz tasvirlarda lezyonlarni vizualizatsiya qilish imkonini beradi. Ma’lumotlar hajmi past boʻlsa, namunalar xilma-xilligini yaxshilash uchun besh tomonlama ma’lumotlarni yaxshilash amalga oshiriladi. Besh marta oʻzaro tekshirish tajribasi oʻrtacha 85,3% tasniflash aniqligini koʻrsatadi va oltita ilgʻor tasniflash modeli bilan taqqoslashni biz taklif qilayotgan tarmoq yaxshiroq ishlashga ega ekanligini koʻrsatadi.
Информация о статье
Это произведение доступно по лицензии Creative Commons «Attribution» («Атрибуция») 4.0 Всемирная.
Библиографические ссылки
World Health Organization, WHO coronavirus disease (COVID-19) Dashboard, Available, https://covid19. who.int/?gclid=Cj0KCQjwtZH7BRDzARIsAGjbK2ZXWRpJROEl97HGmSOx0_ydkVbc02Ka1FlcysGjEI7hnaIeR6xWhr4aAu57EALw_wcB, 2022.
Y. Yuan, C. Ming, Y.C. Lo. “Automatic skin lesion segmentation using deep fully convolutional networks with jaccard distance.” IEEE transactions on medical imaging 36.9 (2017): 1876-1886.
P. Liskowski, K. Krzysztof. “Segmenting retinal blood vessels with deep neural networks.” IEEE transactions on medical imaging 35.11 (2016): 2369-2380.
H. Fu, J. Cheng, Y. Xu, D. W. K. Wong, J. Liu, X. Cao, “Joint optic disc and cup segmentation based on multi-label deep network and polar transformation,” IEEE transactions on medical imaging, 37(7), (2018):1597-1605.
H. Fu, Y . Xu, S. Lin, X. Zhang, D. W. K. Wong, J. Liu, A. F. Frangi, M. Baskaran, and T. Aung, “Segmentation and quantification for angle-closure glau-coma assessment in anterior segment OCT.” IEEE transactions on medical imaging 36.9 (2017): 1930-1938.
M. Anthimopoulos, S. Christodoulidis, L. Ebner, A. Christe, and S. Mougiakakou, “Lung pattern classification for interstitial lung diseases using a deep convolutional neural network.” IEEE transactions on medical imaging 35.5 (2016): 1207-1216.
P. Kumar, M. Grewal, and M. M. Srivastava, “Boosted cascaded convnets for multilabel classification of thoracic diseases in chest radiographs,” In International conference image analysis and recognition, pp. 546-552. Springer, Cham, 2018.
P. Rajpurkar, J. Irvin, K. Zhu, B. Yang, H. Mehta, T. Duan, D. Ding, A. Bagul, C. Langlotz, K. Shpanskayaet al., “Chexnet: Radiologist-level pneumonia detection on chest x-rays with deep learning.” arXiv preprint arXiv:1711.05225 (2017).
M. Togacar, B. Ergen, and Z. Comert, "COVID-19 detection using deep learning models to exploit Social Mimic Optimization and structured chest X-ray images using fuzzy color and stacking approaches," Computers in Biology and Medicine, vol. 121, p. 12, Article ID: 103805, Jun, 2020.
J. P. Cohen, L. Dao, P. Morrison, K. Roth, Y. Bengio, B. Y. Shen, et al., "Predicting COVID-19 Pneumonia Severity on Chest X-ray With Deep Learning," Cureus, vol. 12, p. 10, Article ID: e9448, Jul, 2020.
Q. Q. Ni, Z. Y. Sun, L. Qi, W. Chen, Y. Yang, L. Wang, et al., "A deep learning approach to characterize 2019 coronavirus disease (COVID-19) pneumonia in chest CT images," European Radiology, p. 11, 2020.
H. Ko, H. Chung, W. S. Kang, K. W. Kim, Y. Shin, S. J. Kang, et al., "COVID-19 Pneumonia Diagnosis Using a Simple 2D Deep Learning Framework With a Single Chest CT Image: Model Development and V alidation," Journal of Medical Internet Research, vol. 22, p. 13, Article ID: e19569, Jun, 2020.
R. Liao, S. Miao, P. de Tournemire, S. Grbic, A. Kamen, T. Mansi, and D. Comaniciu, “An artificial agent for robust image registration.” In Proceedings of the AAAI conference on artificial intelligence 31(1) (2017), pp. 4168–4175.1
H. Ko, H. Chung, W. S. Kang, K. W. Kim, Y. Shin, S. J. Kang, et al., COVID-19 pneumonia diagnosis using a simple 2D deep learning framework with a single chest CT image: model development and validation. Journal of medical Internet research, 22(6) (2020):e19569.P. Li and G. Liu, "Pathological Brain Detection via Wavelet Packet Tsallis Entropy and Real-Coded Biogeography-based Optimiza-tion," Fundamenta Informaticae, vol. 151, pp. 275-291, 2017.
Hussain, et al., Machine-learning classification of texture features of portable chest X-ray accurately classifies COVID-19 lung infection, BioMedical Engineering OnLine 19 (1) (2020) 1–18.