In this study, digital image processing and a convolutional neural network (CNN) were integrated to automatically detect mitral stenosis based on 32 echocardiogram videos. By applying data augmentation, the training dataset was expanded, and the CNN architecture, consisting of four convolutional blocks, was trained for binary classification. The model achieved 92% accuracy and 0.26 loss on the test dataset. The results demonstrated that these approaches can serve as an effective auxiliary tool in clinical diagnosis.
| Mualliflar | Djurayeva, Nigora, Raximov, Mexriddin |
|---|---|
| Jurnal | Al-Farg'oniy avlodlari |
| Nashr sanasi | 2025-10-03 |
| Son | 3 |
| Betlar | 94-100 |
| Til | Rus |
mitral stenoz, exokardiogramma, raqamli tasvirlarni qayta ishlash, klassifikatsiya, konvolyutsion neyron tarmoq, avtomatlashtirilgan diagnostika.
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