YOLOV5 MODELI YORDAMIDA REAL VAQTDA TIBBIY ANOMALIYALARNI ANIQLASH TIZIMI
Keywords:
YOLOv5, anomaliya, tibbiy tasvirlar, sun’iy intellekt, real vaqtli aniqlash, konvolyutsion neyron tarmoq, rentgen, MRT, CT, AI-tibbiyot, tashxis, segmentatsiya, bounding box.Abstract
Tibbiyot sohasida sun’iy intellekt vositalarining joriy etilishi tashxis jarayonini avtomatlashtirish, vaqtni tejash va inson xatolarini kamaytirish imkonini bermoqda. Ushbu maqolada real vaqtda tibbiy tasvirlar orqali anomaliyalarni aniqlashda mashinaviy ko‘rish texnologiyalaridan biri bo‘lgan YOLOv5 (You Only Look Once version 5) modelining qo‘llanilishi tahlil qilinadi. Kompyuter ko‘rishning ilg‘or yondashuvi sifatida YOLOv5 nafaqat samarador, balki yengil va real vaqtda ishlash imkoniyatiga ega model hisoblanadi. Maqolada anomaliyalar (o‘simtalar, nuqsonlar, to‘qima deformatsiyalari) tasnifi, YOLOv5 modelining texnik arxitekturasi, o‘qitish bosqichlari, ishlatilgan datasetlar, tajriba natijalari, xatolik darajalari va grafik ko‘rinishlar asosida ilmiy tahlil keltirilgan. Shuningdek, modellashtirishda ishlatilgan formulalar va real amaliy holatlarga tatbiq imkoniyatlari yoritilgan.
References
1. Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You only look once: Unified, real-time object detection. Proceedings of the IEEE conference on computer vision and pattern recognition, 779-788.
2. Bojarski, M., et al. (2016). End to end learning for self-driving cars. arXiv preprint arXiv:1604.07316.
3. Zhang, Z., & Liu, Z. (2020). YOLOv5: Real-time object detection and its applications. IEEE Access, 8, 192429-192438.
4. Liao, S., et al. (2019). A survey of machine learning for medical image analysis. Medical Image Analysis, 58, 101539.
5. Choi, J. W., & Kim, D. (2020). Real-time lung nodule detection using YOLOv5 in CT images. Journal of Computational Imaging, 6(4), 304-314.
6. Litjens, G., et al. (2017). A survey of deep learning in medical image analysis. Medical Image Analysis, 42, 60-88.
7. Muhammad, F., & Qamar, U. (2021). Real-time medical image processing with YOLO. Computers in Biology and Medicine, 135, 104528.
8. Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE conference on computer vision and pattern recognition, 580-587.
