KT TASVIRLARIDA O‘PKA TUGUNLARINI SEGMENTATSIYA QILISH UCHUN SUN’IY INTELLEKT YONDASHUVLARINING SAMARADORLIGI
Keywords:
O‘pka tugunlari, KT tasvirlari, segmentatsiya, sun’iy intellekt, U-Net, Mask R-CNN, 3D CNN.Abstract
O‘pka tugunlarini aniqlash va segmentatsiya qilish o‘pka saratonini erta bosqichda tashxislashda muhim ahamiyatga ega. KT tasvirlari diagnostikada keng qo‘llanilsa-da, ularni qo‘lda tahlil qilish ko‘p vaqt talab qiladi va subyektiv xatolarga olib kelishi mumkin. Sun’iy intellekt algoritmlari, xususan chuqur o‘rganish asosidagi segmentatsiya modellarining qo‘llanilishi bu jarayonni avtomatlashtirish imkonini beradi. Ushbu maqolada U-Net [1], Mask R-CNN [2] va 3D CNN [3] kabi yondashuvlarning samaradorligi adabiyotlar va tajriba natijalari asosida tahlil qilinadi.
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References
1. Ronneberger O, Fischer P, Brox T. U-Net: Convolutional Networks for Biomedical Image Segmentation. MICCAI, 2015.
2. He K, Gkioxari G, Dollár P, Girshick R. Mask R-CNN. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017.
3. Zhu W, Liu C, et al. Deep learning for lung cancer detection on CT images. Medical Image Analysis, 2018.
4. Menglu Liu, Junyu Dong, Xinghui Dong, Hui Yu, Lin Qi. Segmentation of Lung Nodule in CT Images Based on Mask R-CNN. Ocean University of China & University of Portsmouth, 2023.
5. Lijia Zhi, Wujun Jiang, Shaomin Zhang, Tao Zhou. Deep neural network pulmonary nodule segmentation methods for CT images: Literature review and experimental comparisons. Computers in Biology and Medicine, 2023
