AlMohimeed Abdulaziz, Shehata Mohamed, El-Rashidy Nora, Mostafa Sherif, Samy Talaat Amira, Saleh Hager
College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 13318, Saudi Arabia.
Bioengineering Department, Speed School of Engineering, University of Louisville, Louisville, KY 40292, USA.
Bioengineering (Basel). 2024 Jul 18;11(7):729. doi: 10.3390/bioengineering11070729.
Cervical cancer (CCa) is the fourth most prevalent and common cancer affecting women worldwide, with increasing incidence and mortality rates. Hence, early detection of CCa plays a crucial role in improving outcomes. Non-invasive imaging procedures with good diagnostic performance are desirable and have the potential to lessen the degree of intervention associated with the gold standard, biopsy. Recently, artificial intelligence-based diagnostic models such as Vision Transformers (ViT) have shown promising performance in image classification tasks, rivaling or surpassing traditional convolutional neural networks (CNNs). This paper studies the effect of applying a ViT to predict CCa using different image benchmark datasets. A newly developed approach (ViT-PSO-SVM) was presented for boosting the results of the ViT based on integrating the ViT with particle swarm optimization (PSO), and support vector machine (SVM). First, the proposed framework extracts features from the Vision Transformer. Then, PSO is used to reduce the complexity of extracted features and optimize feature representation. Finally, a softmax classification layer is replaced with an SVM classification model to precisely predict CCa. The models are evaluated using two benchmark cervical cell image datasets, namely SipakMed and Herlev, with different classification scenarios: two, three, and five classes. The proposed approach achieved 99.112% accuracy and 99.113% F1-score for SipakMed with two classes and achieved 97.778% accuracy and 97.805% F1-score for Herlev with two classes outperforming other Vision Transformers, CNN models, and pre-trained models. Finally, GradCAM is used as an explainable artificial intelligence (XAI) tool to visualize and understand the regions of a given image that are important for a model's prediction. The obtained experimental results demonstrate the feasibility and efficacy of the developed ViT-PSO-SVM approach and hold the promise of providing a robust, reliable, accurate, and non-invasive diagnostic tool that will lead to improved healthcare outcomes worldwide.
宫颈癌（CCa）是全球影响女性的第四大常见癌症，其发病率和死亡率呈上升趋势。因此，早期检测宫颈癌对改善治疗结果起着至关重要的作用。具有良好诊断性能的非侵入性成像程序是理想的，并且有可能降低与金标准活检相关的干预程度。最近，基于人工智能的诊断模型，如视觉Transformer（ViT），在图像分类任务中表现出了有前景的性能，可与传统卷积神经网络（CNN）相媲美或超越它们。本文研究了应用ViT使用不同图像基准数据集预测宫颈癌的效果。提出了一种新开发的方法（ViT-PSO-SVM），通过将ViT与粒子群优化（PSO）和支持向量机（SVM）相结合来提高ViT的结果。首先，所提出的框架从视觉Transformer中提取特征。然后，使用PSO降低提取特征的复杂性并优化特征表示。最后，用SVM分类模型替换softmax分类层以精确预测宫颈癌。使用两个基准宫颈细胞图像数据集，即SipakMed和Herlev，在不同的分类场景下（两类、三类和五类）对模型进行评估。所提出的方法在SipakMed两类分类中实现了99.112%的准确率和99.113%的F1分数，在Herlev两类分类中实现了97.778%的准确率和97.805%的F1分数，优于其他视觉Transformer、CNN模型和预训练模型。最后，GradCAM用作可解释人工智能（XAI）工具，以可视化和理解给定图像中对模型预测重要的区域。获得的实验结果证明了所开发的ViT-PSO-SVM方法的可行性和有效性，并有望提供一种强大、可靠、准确且非侵入性的诊断工具，这将改善全球的医疗保健结果。
