Abstract
Cervical cancer stands as a predominant cause of female mortality, underscoring the need for regular screenings to enable early diagnosis and preemptive treatment of pre-cancerous conditions. The transformation zone in the cervix, where cellular differentiation occurs, plays a critical role in the detection of abnormalities. Colposcopy has emerged as a pivotal tool in cervical cancer prevention since it provides a meticulous examination of cervical abnormalities. However, challenges in visual evaluation necessitate the development of Computer Aided Diagnosis (CAD) systems. We propose a novel CAD system that combines the strengths of various deep-learning descriptors (ResNet50, ResNet101, and ResNet152) with appropriate feature normalization (min-max) as well as feature reduction technique (LDA). The combination of different descriptors ensures that all the features (low-level like edges and colour, high-level like shape and texture) are captured, feature normalization prevents biased learning, and feature reduction avoids overfitting. We do experiments on the IARC dataset provided by WHO. The dataset is initially segmented and balanced. Our approach achieves exceptional performance in the range of 97%-100% for both the normal-abnormal and the type classification. A competitive approach for type classification on the same dataset achieved 81%-91% performance.
Abstract (translated)
宫颈癌是女性死亡的主要原因，这凸显了定期筛查的重要性，以实现早期诊断和预防性治疗。宫颈转变成分的关键区域，在该区域发生细胞分化时，对于异常情况的检测至关重要。自从宫颈内窥镜作为一种关键工具在宫颈癌预防中得到广泛应用以来，它提供了对宫颈异常的详细检查。然而，视觉评估方面的挑战迫使开发了计算机辅助诊断（CAD）系统。我们提出了一个结合各种深度学习描述符（ResNet50、ResNet101 和 ResNet152）的适当特征归一化（min-max）以及特征减少技术（LDA）的新颖 CAD 系统。不同描述器的组合确保所有特征（低级如边缘和颜色，高级如形状和纹理）都被捕捉到，特征归一化防止了偏差学习，特征减少避免了过拟合。我们在世界卫生组织（WHO）提供的 IARC 数据集上进行实验。该数据集最初进行分割和平衡。我们的方法在正常-异常和类型分类上取得了非常出色的成绩，达到97%-100%。在同一数据集上，对于类型分类的竞争方法获得了81%-91%的性能。
URL
https://arxiv.org/abs/2405.01600
