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Cai, Peiya; Yang, Tiantian; Xie, Qinglai; Liu, Peizhong; Li, Ping
Journal of clinical ultrasound, July/August 2024, Letnik: 52, Številka: 6Journal Article
Purpose Uterine fibroids (UF) are the most frequent tumors in ladies and can pose an enormous threat to complications, such as miscarriage. The accuracy of prognosis may also be affected by way of doctor inexperience and fatigue, underscoring the want for automatic classification fashions that can analyze UF from a giant wide variety of images. Methods A hybrid model has been proposed that combines the MobileNetV2 community and deep convolutional generative adversarial networks (DCGAN) into useful resources for medical practitioners in figuring out UF and evaluating its characteristics. Real‐time automated classification of UF can aid in diagnosing the circumstance and minimizing subjective errors. The DCGAN science is utilized for superior statistics augmentation to create first‐rate UF images, which are labeled into UF and non‐uterine‐fibroid (NUF) classes. The MobileNetV2 model then precisely classifies the photos based totally on this data. Results The overall performance of the hybrid model contrasts with different models. The hybrid model achieves a real‐time classification velocity of 40 frames per second (FPS), an accuracy of 97.45%, and an F1 rating of 0.9741. Conclusion By using this deep learning hybrid approach, we address the shortcomings of the current classification methods of uterine fibroid. We propose a novel hybrid model combining the MobileNetV2 network and deep convolutional generative adversarial networks (DCGAN) to support physicians in identifying uterine fibroid (UF) and assessing its characteristics. Real‐time automatic classification of the UF can assist doctors in diagnosis and reduce subjective randomness.
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Leto | Faktor vpliva | Izdaja | Kategorija | Razvrstitev | ||||
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JCR | SNIP | JCR | SNIP | JCR | SNIP | JCR | SNIP |
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Vir: Osebne bibliografije
in: SICRIS
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