TY -的A2 Bhattacharya丝薇盟——Narayan Vipul盟——购物中心,一生Kumar盟——Alkhayyat Ahmed AU -阿布,Kumar AU -库马尔,桑杰AU - Pandey,普拉卡什PY - 2023 DA - 2023/05/02 TI - Enhance-Net:一种方法来提高性能的基于实时医学图像的深度学习模型SP - 8276738六世- 2023 AB -实时医学图像分类是世界上一个复杂的问题。利用物联网技术在医疗应用程序确保医疗保健行业通过自动化提高治疗质量的同时降低成本和资源的优化。在对医学图像进行分类深度学习是至关重要的,是通过人工智能。深入学习算法允许放射科医生和整形外科医生,让他们的生活更容易通过提供更快和更准确的结果。尽管如此,经典的深度学习技术已达到其性能极限。由于这些原因,在本研究中,我们审查替代增强策略提高深层神经网络的性能提供一个最优解称为Enhance-Net。可以分类实验分成六个不同的阶段。Champion-Net从池中被选为一个深度学习模型的基准深度学习模型(EfficientNet: B0, MobileNet、ResNet-18 VGG-19)。这个阶段帮助选择最优模型。在第二步中,Champion-Net测试各种决议。 This stage helps conclude dataset resolution and improves Champion-Net performance. The next stage extracts green channel data. In the fourth step, Champion-Net combines with image enhancement algorithms CLAHE, HEF, and UM. This phase serves to improve Enhance-performance. The next stage compares the Enhance-Net findings to the lightness order error (LoE). In Enhance-Net models, the current study combines image enhancement and green channel with Champion-Net. In the final step, radiologists and orthopaedic surgeons use the trained model for real-time medical image prediction. The study effort uses the musculoskeletal radiograph-bone classification (MURA-BC) dataset. Classification accuracy of Enhance-Net was determined for the train and test datasets. These models obtained 98.02 percent, 94.79 percent, and 94.61 percent accuracy, respectively. The 96.74% accuracy was achieved during real-time testing with the unseen dataset. SN - 1687-725X UR - https://doi.org/10.1155/2023/8276738 DO - 10.1155/2023/8276738 JF - Journal of Sensors PB - Hindawi KW - ER -