Marques Rodrigo, Santos Jaime, André Alexandra, Silva José
Faculdade de Ciências e Tecnologias, Department of Physics, University of Coimbra, Rua Larga, 3004-516 Coimbra, Portugal.
Department of Electrical and Computers Engineering, CEMMPRE-ARISE, University of Coimbra, Polo II, Rua Sílvio Lima, 3030-970 Coimbra, Portugal.
Sensors (Basel). 2024 Nov 27;24(23):7568. doi: 10.3390/s24237568.
The prevalence of fatty liver disease is on the rise, posing a significant global health concern. If left untreated, it can progress into more serious liver diseases. Therefore, accurately diagnosing the condition at an early stage is essential for more effective intervention and management. This study uses images acquired via ultrasound and elastography to classify liver steatosis using classical machine learning classifiers, including random forest and support vector machine, as well as deep learning architectures, such as ResNet50V2 and DenseNet-201. The neural network demonstrated the most optimal performance, achieving an F1 score of 99.5% on the ultrasound dataset, 99.2% on the elastography dataset, and 98.9% on the mixed dataset. The results from the deep learning approach are comparable to those of machine learning, despite objectively not achieving the highest results. This research offers valuable insights into the domain of medical image classification and advocates the integration of advanced machine learning and deep learning technologies in diagnosing steatosis.
脂肪肝疾病的患病率正在上升，这成为一个重大的全球健康问题。如果不加以治疗，它可能会发展成更严重的肝脏疾病。因此，早期准确诊断病情对于更有效的干预和管理至关重要。本研究使用通过超声和弹性成像获取的图像，利用经典机器学习分类器（包括随机森林和支持向量机）以及深度学习架构（如ResNet50V2和DenseNet - 201）对肝脏脂肪变性进行分类。神经网络表现出最优化的性能，在超声数据集上的F1分数达到99.5%，在弹性成像数据集上为99.2%，在混合数据集上为98.9%。尽管客观上深度学习方法未取得最高结果，但其结果与机器学习的结果相当。这项研究为医学图像分类领域提供了有价值的见解，并提倡在诊断脂肪变性中整合先进的机器学习和深度学习技术。
