INFORMATIONAL-TECHNICAL SYSTEM FOR THE AUTOMATIZED LAPAROSCOPIC DIAGNOSTICS
Науковий журнал «Радіоелектроніка, інформатика, управління»
Переглянути архів Інформація| Поле | Співвідношення | |
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INFORMATIONAL-TECHNICAL SYSTEM FOR THE AUTOMATIZED LAPAROSCOPIC DIAGNOSTICS |
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Lyashenko, A. V.; Odessa National Medical University, Odessa, Ukraine Bayazitov, N. R.; Odessa National Medical University, Odessa, Ukraine Godlevsky, L. S.; Odessa National Medical University, Odessa, Ukraine Bayazitov, D. N.; Odessa National Medical University, Odessa, Ukraine Buzynovskiy, A. V.; Odessa Regional Hospital, Odessa, Ukraine |
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automatic recognition of images, laparoscopic video-images, Haara features. |
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The problem of automatic recognition – diagnostics of cirrhotic and metastatic liver damage has been solved on the basis of laparoscopic<br />images analysis. The object of the investigation was confined to the process of diagnostic automatic system of laparoscopic images recognition building up. The subject of investigation was confined to composing of training images for the learning of cascade Haar’s classificatory. The establishing of the system of decision support for laparoscopic surgeons was the aim of the investigation. The automatic diagnostic technology was developed on the basis of Haar’s features usage. The classification of images was performed using cascade classificator exploration, and 1000 positive images along with 500 negative ones have been used for the learning . It was established that the sensitivity of cirrhosis of the liver diagnostics was 68,8% and exceeded that one which was determined after expert analysis (31,0%) (P<0,01). The sensitivity of metastatic damage was 80,0% and 46,7% after developed and expert diagnostics were performed correspondently (P<0,02).Besides, the specificity was also elevated – from 52,5% after expert diagnostics up to 85,0% (P<0,01) after developed method. The net increasing of both positive prognostic index (from 42,4% up to 80,0%, P<0,01), as well as negative one (from 56,8% up to 87,2%, P<0,01) was also observed. In accordance to results of tests, the AUC ROC for cascade classificator was 0,891, while such one for expert analysis was 0,723. That is in favor for higher effectiveness of cascade classificator. The worked out technology is recommended for laparoscopic surgery clinical exploration. |
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Zaporizhzhya National Technical University 2017-01-20 00:00:00 |
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application/pdf http://ric.zntu.edu.ua/article/view/90650 |
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Radio Electronics, Computer Science, Control; No 4 (2016): Radio Electronics, Computer Science, Control |
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uk |
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Copyright (c) 2017 A. V. Lyashenko, N. R. Bayazitov, L. S. Godlevsky, D. N. Bayazitov, A. V. Buzynovskiy |
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