As the capabilities of information technologies develop, the effectiveness of using the capabilities of CNN convolutional neural networks, an algorithm with a high level of accuracy, and early prediction of various diseases in blood cell images is high. This study proposes an effective method for early detection of acute lymphoblastic leukemia in blood cell images using Support Vector Machines (SVM) algorithm. In this method, a pattern of cells is recognized and used to identify cell markers specific to leukemia. This algorithm is used to match leukemia to a single marker in the cell image. Comparing SNN convolutional neural network algorithms with random forest (RF), Bayesian classifier, Support Vector Machines (SVM) and K nearest neighbor (KNN) algorithms, the results obtained by Support Vector Machines (SVM) were found to be 90.9% efficient.
As the capabilities of information technologies develop, the effectiveness of using the capabilities of CNN convolutional neural networks, an algorithm with a high level of accuracy, and early prediction of various diseases in blood cell images is high. This study proposes an effective method for early detection of acute lymphoblastic leukemia in blood cell images using Support Vector Machines (SVM) algorithm. In this method, a pattern of cells is recognized and used to identify cell markers specific to leukemia. This algorithm is used to match leukemia to a single marker in the cell image. Comparing SNN convolutional neural network algorithms with random forest (RF), Bayesian classifier, Support Vector Machines (SVM) and K nearest neighbor (KNN) algorithms, the results obtained by Support Vector Machines (SVM) were found to be 90.9% efficient.
По мере развития возможностей информационных технологий эффективность использования возможностей нейронных сетей CNN, алгоритма с высоким уровнем точности и раннего прогнозирования различных заболеваний по изображениям клеток крови высока. В этом исследовании предлагается эффективный метод раннего выявления острого лимфобластного лейкоза на изображениях клеток крови с использованием алгоритма опорных векторов (SVM). В этом методе распознается структура клеток и используется для идентификации клеточных маркеров, специфичных для лейкемии. Этот алгоритм используется для сопоставления лейкемии с одним маркером на изображении клетки. Сравнивая алгоритмы нейронной сети SNN со случайным лесом (RF), байесовским классификатором, алгоритмами машин опорных векторов (SVM) и K ближайших соседей (KNN), результаты, полученные с помощью машин опорных векторов (SVM), оказались эффективными на 90,9%.
| № | Muallifning F.I.Sh. | Lavozimi | Tashkilot nomi |
|---|---|---|---|
| 1 | Iskandarova S.. | Dotsent | TATU |
| 2 | Tulaganova F.. | Dotsent | TATU |
| 3 | Akbarova M.. | Dotsent | TATU |
| 4 | Xaitova X.. | Dotsent | TATU |
| № | Havola nomi |
|---|---|
| 1 | J. Carreira, R. Caseiro, J. Batista, and C. Sminchisescu. Semantic segmentation with second-order pooling. In ECCV, 2012. |
| 2 | R. Caruana. Multitask learning. Machine learning, 28(1), 1997. |
| 3 | K. Chatfield, K. Simonyan, A. Vedaldi, and A. Zisserman. Return of the devil in the details: Delving deep into convolutional nets. In BMVC, 2014. |
| 4 | J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. FeiFei. ImageNet: A large-scale hierarchical image database. In CVPR, 2009. |
| 5 | E. Denton, W. Zaremba, J. Bruna, Y. LeCun, and R. Fergus. Exploiting linear structure within convolutional networks for efficient evaluation. In NIPS, 2014. |
| 6 | D. Erhan, C. Szegedy, A. Toshev, and D. Anguelov. Scalable object detection using deep neural networks. In CVPR, 2014 3 |
| 7 | M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman. The PASCAL Visual Object Classes (VOC) Challenge. IJCV, 2010. |
| 8 | P. Felzenszwalb, R. Girshick, D. McAllester, and D. Ramanan. Object detection with discriminatively trained part based models. TPAMI, 2010. |
| 9 | R. Girshick, J. Donahue, T. Darrell, and J. Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. In CVPR, 2014. |
| 10 | R. Girshick, J. Donahue, T. Darrell, and J. Malik. Region based convolutional networks for accurate object detection and segmentation. TPAMI, 2015. |
| 11 | K. He, X. Zhang, S. Ren, and J. Sun. Spatial pyramid pooling in deep convolutional networks for visual recognition. ECCV, 2014 1-7. |
| 12 | J. H. Hosang, R. Benenson, P. Doll´ makes for effective detection proposals? arXiv preprint arXiv:1502.05082, 2015. |
| 13 | Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell. Caffe: Convolutional architecture for fast feature embedding. In Proc. of the ACM International Conf. on Multimedia, 2014. |
| 14 | A. Krizhevsky, I. Sutskever, and G. Hinton. ImageNet classification with deep convolutional neural networks. In NIPS, 2012. |
| 15 | S. Lazebnik, C. Schmid, and J. Ponce. Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In CVPR, 2006. |
| 16 | Y. LeCun, B. Boser, J. Denker, D. Henderson, R. Howard, W. Hubbard, and L. Jackel. Backpropagation applied to handwritten zip code recognition. Neural Comp., 1989. |
| 17 | M. Lin, Q. Chen, and S. Yan. Network in network. In ICLR, 2014. |
| 18 | T. Lin, M. Maire, S. Belongie, L. Bourdev, R. Girshick, J. Hays, P. Perona, D. Ramanan, P. Doll´ nick. Microsoft COCO: common objects in context. arXive-prints, arXiv:1405.0312 [cs.CV], 2014. |