Department of Electrical Engineering, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia.
Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
J Digit Imaging. 2023 Aug;36(4):1460-1479. doi: 10.1007/s10278-023-00828-7. Epub 2023 May 5.
An automated diagnosis system is crucial for helping radiologists identify brain abnormalities efficiently. The convolutional neural network (CNN) algorithm of deep learning has the advantage of automated feature extraction beneficial for an automated diagnosis system. However, several challenges in the CNN-based classifiers of medical images, such as a lack of labeled data and class imbalance problems, can significantly hinder the performance. Meanwhile, the expertise of multiple clinicians may be required to achieve accurate diagnoses, which can be reflected in the use of multiple algorithms. In this paper, we present Deep-Stacked CNN, a deep heterogeneous model based on stacked generalization to harness the advantages of different CNN-based classifiers. The model aims to improve robustness in the task of multi-class brain disease classification when we have no opportunity to train single CNNs on sufficient data. We propose two levels of learning processes to obtain the desired model. At the first level, different pre-trained CNNs fine-tuned via transfer learning will be selected as the base classifiers through several procedures. Each base classifier has a unique expert-like character, which provides diversity to the diagnosis outcomes. At the second level, the base classifiers are stacked together through neural network, representing the meta-learner that best combines their outputs and generates the final prediction. The proposed Deep-Stacked CNN obtained an accuracy of 99.14% when evaluated on the untouched dataset. This model shows its superiority over existing methods in the same domain. It also requires fewer parameters and computations while maintaining outstanding performance.
自动化诊断系统对于帮助放射科医生高效地识别脑部异常至关重要。深度学习的卷积神经网络（CNN）算法具有自动提取特征的优势，这对自动化诊断系统非常有益。然而，基于卷积神经网络的医学图像分类器存在一些挑战，例如缺乏标记数据和类别不平衡问题，这些问题可能会显著影响性能。同时，可能需要多位临床医生的专业知识才能实现准确的诊断，这可以反映在使用多种算法上。在本文中，我们提出了 Deep-Stacked CNN，这是一种基于堆叠泛化的深度异构模型，利用不同基于卷积神经网络的分类器的优势。该模型旨在提高在没有机会对大量数据进行单 CNN 训练的情况下，对多类脑部疾病分类任务的鲁棒性。我们提出了两个学习过程的层次来获得所需的模型。在第一个层次，通过迁移学习微调的不同预训练 CNN 将通过几个步骤被选为基础分类器。每个基础分类器都具有独特的专家式特征，为诊断结果提供多样性。在第二个层次，通过神经网络将基础分类器堆叠在一起，代表最佳地组合它们的输出并生成最终预测的元学习者。在未触及的数据集上进行评估时，所提出的 Deep-Stacked CNN 获得了 99.14%的准确率。该模型在同一领域的现有方法中显示出了优越性。它还需要更少的参数和计算，同时保持出色的性能。
