Ajilisa O A, Jagathy Raj V P, Sabu M K
Department of Computer Applications, Cochin University of Science and Technology, South Kalamassery, Kochi 682022, Kerala, India.
School of Management Studies, Cochin University of Science and Technology, South Kalamassery, Kochi 682022, Kerala, India.
Diagnostics (Basel). 2023 Jul 24;13(14):2463. doi: 10.3390/diagnostics13142463.
In the past few years, deep learning has gained increasingly widespread attention and has been applied to diagnosing benign and malignant thyroid nodules. It is difficult to acquire sufficient medical images, resulting in insufficient data, which hinders the development of an efficient deep-learning model. In this paper, we developed a deep-learning-based characterization framework to differentiate malignant and benign nodules from the thyroid ultrasound images. This approach improves the recognition accuracy of the inception network by combining squeeze and excitation networks with the inception modules. We have also integrated the concept of multi-level transfer learning using breast ultrasound images as a bridge dataset. This transfer learning approach addresses the issues regarding domain differences between natural images and ultrasound images during transfer learning. This paper aimed to investigate how the entire framework could help radiologists improve diagnostic performance and avoid unnecessary fine-needle aspiration. The proposed approach based on multi-level transfer learning and improved inception blocks achieved higher precision (0.9057 for the benign class and 0.9667 for the malignant class), recall (0.9796 for the benign class and 0.8529 for malignant), and F1-score (0.9412 for benign class and 0.9062 for malignant class). It also obtained an AUC value of 0.9537, which is higher than that of the single-level transfer learning method. The experimental results show that this model can achieve satisfactory classification accuracy comparable to experienced radiologists. Using this model, we can save time and effort as well as deliver potential clinical application value.
在过去几年中，深度学习越来越受到广泛关注，并已应用于甲状腺良恶性结节的诊断。获取足够的医学图像很困难，导致数据不足，这阻碍了高效深度学习模型的发展。在本文中，我们开发了一个基于深度学习的特征框架，用于从甲状腺超声图像中区分恶性和良性结节。这种方法通过将挤压与激励网络和初始模块相结合，提高了初始网络的识别准确率。我们还整合了多级迁移学习的概念，使用乳腺超声图像作为桥梁数据集。这种迁移学习方法解决了迁移学习过程中自然图像和超声图像之间的领域差异问题。本文旨在研究整个框架如何帮助放射科医生提高诊断性能并避免不必要的细针穿刺。基于多级迁移学习和改进的初始模块提出的方法实现了更高的精度（良性类别为0.9057，恶性类别为0.9667）、召回率（良性类别为0.9796，恶性类别为0.8529）和F1分数（良性类别为0.9412，恶性类别为0.9062）。它还获得了0.9537的AUC值，高于单级迁移学习方法。实验结果表明，该模型可以实现与经验丰富的放射科医生相当的令人满意的分类准确率。使用该模型，我们可以节省时间和精力，并提供潜在的临床应用价值。
