Sharma Rohit, Mahanti Gautam Kumar, Panda Ganapati, Rath Adyasha, Dash Sujata, Mallik Saurav, Hu Ruifeng
Department of Electronics and Communication Engineering, National Institute of Technology, Durgapur 713209, India.
Department of Electronics and Communication Engineering, C.V. Raman Global University, Bhubaneswar 752054, India.
J Imaging. 2023 Aug 27;9(9):173. doi: 10.3390/jimaging9090173.
Computer-assisted diagnostic systems have been developed to aid doctors in diagnosing thyroid-related abnormalities. The aim of this research is to improve the diagnosis accuracy of thyroid abnormality detection models that can be utilized to alleviate undue pressure on healthcare professionals. In this research, we proposed deep learning, metaheuristics, and a MCDM algorithms-based framework to detect thyroid-related abnormalities from ultrasound and histopathological images. The proposed method uses three recently developed deep learning techniques (DeiT, Swin Transformer, and Mixer-MLP) to extract features from the thyroid image datasets. The feature extraction techniques are based on the Image Transformer and MLP models. There is a large number of redundant features that can overfit the classifiers and reduce the generalization capabilities of the classifiers. In order to avoid the overfitting problem, six feature transformation techniques (PCA, TSVD, FastICA, ISOMAP, LLE, and UMP) are analyzed to reduce the dimensionality of the data. There are five different classifiers (LR, NB, SVC, KNN, and RF) evaluated using the 5-fold stratified cross-validation technique on the transformed dataset. Both datasets exhibit large class imbalances and hence, the stratified cross-validation technique is used to evaluate the performance. The MEREC-TOPSIS MCDM technique is used for ranking the evaluated models at different analysis stages. In the first stage, the best feature extraction and classification techniques are chosen, whereas, in the second stage, the best dimensionality reduction method is evaluated in wrapper feature selection mode. Two best-ranked models are further selected for the weighted average ensemble learning and features selection using the recently proposed meta-heuristics FOX-optimization algorithm. The PCA+FOX optimization-based feature selection + random forest model achieved the highest TOPSIS score and performed exceptionally well with an accuracy of 99.13%, F2-score of 98.82%, and AUC-ROC score of 99.13% on the ultrasound dataset. Similarly, the model achieved an accuracy score of 90.65%, an F2-score of 92.01%, and an AUC-ROC score of 95.48% on the histopathological dataset. This study exploits the combination novelty of different algorithms in order to improve the thyroid cancer diagnosis capabilities. This proposed framework outperforms the current state-of-the-art diagnostic methods for thyroid-related abnormalities in ultrasound and histopathological datasets and can significantly aid medical professionals by reducing the excessive burden on the medical fraternity.
计算机辅助诊断系统已被开发出来，以帮助医生诊断甲状腺相关异常。本研究的目的是提高甲状腺异常检测模型的诊断准确性，该模型可用于减轻医疗保健专业人员的不必要压力。在本研究中，我们提出了一种基于深度学习、元启发式算法和多准则决策（MCDM）算法的框架，用于从超声和组织病理学图像中检测甲状腺相关异常。所提出的方法使用三种最近开发的深度学习技术（DeiT、Swin Transformer和Mixer-MLP）从甲状腺图像数据集中提取特征。特征提取技术基于图像Transformer和MLP模型。存在大量冗余特征，这些特征可能会使分类器过拟合，并降低分类器的泛化能力。为了避免过拟合问题，分析了六种特征变换技术（主成分分析（PCA）、截断奇异值分解（TSVD）、快速独立成分分析（FastICA）、等距映射（ISOMAP）、局部线性嵌入（LLE）和非负矩阵分解（UMP））以降低数据的维度。使用5折分层交叉验证技术在变换后的数据集上评估了五种不同的分类器（逻辑回归（LR）、朴素贝叶斯（NB）、支持向量机（SVC）、K近邻（KNN）和随机森林（RF））。两个数据集都表现出较大的类别不平衡，因此，使用分层交叉验证技术来评估性能。MEREC-TOPSIS MCDM技术用于在不同分析阶段对评估模型进行排名。在第一阶段，选择最佳的特征提取和分类技术，而在第二阶段，在包装特征选择模式下评估最佳的降维方法。使用最近提出的元启发式FOX优化算法，进一步选择两个排名最佳的模型进行加权平均集成学习和特征选择。基于PCA+FOX优化的特征选择+随机森林模型在超声数据集上获得了最高的TOPSIS分数，表现非常出色，准确率为99.13%，F2分数为98.82%，AUC-ROC分数为99.13%。同样，该模型在组织病理学数据集上的准确率得分为90.65%，F2分数为92.01%，AUC-ROC分数为95.48%。本研究利用不同算法的组合新颖性来提高甲状腺癌的诊断能力。所提出的框架在超声和组织病理学数据集中优于当前用于甲状腺相关异常的最先进诊断方法，并且可以通过减轻医学界的过度负担来显著帮助医学专业人员。
