Aydın Siyami, Ağar Mehmet, Çakmak Muharrem, Koç Mustafa, Toğaçar Mesut
Department of Thoracic Surgery, Faculty of Medicine, Firat University, 23119 Elazig, Turkey.
Department of Radiology, Faculty of Medicine, Firat University, 23119 Elazig, Turkey.
Diagnostics (Basel). 2024 Dec 26;15(1):26. doi: 10.3390/diagnostics15010026.
: Aspergilloma disease is a fungal mass found in organs such as the sinuses and lungs, caused by the fungus . This disease occurs due to the accumulation of mucus, inflamed cells, and altered blood elements. Various surgical methods are used in clinical settings for the treatment of aspergilloma disease. Expert opinion is crucial for the diagnosis of the disease. Recent advancements in next-generation technologies have made them crucial for disease detection. Deep-learning models, which benefit from continuous technological advancements, are already integrated into current early diagnosis systems. : This study is distinguished by the use of vision transformers (ViTs) rather than traditional deep-learning models. The data used in this study were obtained from patients treated at the Department of Thoracic Surgery at Fırat University. The dataset consists of two class types: aspergilloma disease images and non-aspergilloma disease images. The proposed approach consists of pre-processing, model training, feature extraction, efficient feature selection, feature fusion, and classification processes. In the pre-processing step, unnecessary regions of the images were cropped and data augmentation techniques were applied for model training. Three types of ViT models (vit_base_patch16, vit_large_patch16, and vit_base_resnet50) were used for model training. The feature sets obtained from training the models were merged, and the combined feature set was processed using feature selection methods (2, mRMR, and Relief). Efficient features selected by these methods (2 and mRMR, 2 and Relief, and mRMR and Relief) were combined in certain proportions to obtain more effective feature sets. Machine-learning methods were used in the classification process. : The most successful result in the detection of aspergilloma disease was achieved using Support Vector Machines (SVMs). The SVM method achieved a 99.70% overall accuracy with the cross-validation technique in classification. : These results highlight the benefits of the suggested method for identifying aspergilloma.
曲霉菌瘤病是一种在鼻窦和肺部等器官中发现的真菌团块，由真菌引起。这种疾病是由于黏液、炎症细胞和改变的血液成分积聚而发生的。临床环境中使用各种手术方法治疗曲霉菌瘤病。专家意见对该疾病的诊断至关重要。下一代技术的最新进展使其对疾病检测至关重要。受益于持续技术进步的深度学习模型已经集成到当前的早期诊断系统中。 本研究的独特之处在于使用视觉Transformer（ViT）而不是传统的深度学习模型。本研究中使用的数据来自菲拉特大学胸外科治疗的患者。数据集由两种类别类型组成：曲霉菌瘤病图像和非曲霉菌瘤病图像。所提出的方法包括预处理、模型训练、特征提取、高效特征选择、特征融合和分类过程。在预处理步骤中，裁剪图像的不必要区域并应用数据增强技术进行模型训练。使用三种类型的ViT模型（vit_base_patch16、vit_large_patch16和vit_base_resnet50）进行模型训练。将从模型训练中获得的特征集合并，并使用特征选择方法（2、mRMR和Relief）处理组合特征集。通过这些方法（2和mRMR、2和Relief以及mRMR和Relief）选择的高效特征按一定比例组合以获得更有效的特征集。分类过程中使用了机器学习方法。 在曲霉菌瘤病检测中最成功的结果是使用支持向量机（SVM）获得的。SVM方法在分类中通过交叉验证技术实现了99.70%的总体准确率。 这些结果突出了所建议方法在识别曲霉菌瘤方面的优势。
