Department of Computer Engineering and Technology, Guru Nanak Dev University, Amritsar, India.
Diagn Cytopathol. 2024 Jun;52(6):313-324. doi: 10.1002/dc.25295. Epub 2024 Mar 22.
Cervical cancer, a prevalent and deadly disease among women, comes second only to breast cancer, with over 700 daily deaths. The Pap smear test is a widely utilized screening method for detecting cervical cancer in its early stages. However, this manual screening process is prone to a high rate of false-positive outcomes because of human errors. Researchers are using machine learning and deep learning in computer-aided diagnostic tools to address this issue. These tools automatically analyze and sort cervical cytology and colposcopy images, improving the precision of identifying various stages of cervical cancer.
This article uses state-of-the-art deep learning methods, such as ResNet-50 for categorizing cervical cancer cells to assist medical professionals. The method includes three key steps: preprocessing, segmentation using k-means clustering, and classifying cancer cells. The model is assessed based on performance metrics viz; precision, accuracy, kappa score, precision, sensitivity, and specificity. In the end, the high success rate shows that the ResNet50 model is a valuable tool for timely detection of cervical cancer.
In conclusion, the infected cervical region is pinpointed using spatial K-means clustering and preprocessing operations. This sequence of actions is followed by a progressive learning technique. The Progressive Learning technique then proceeded through several stages: Stage 1 with 64 × 64 images, Stage 2 with 224 × 224 images, Stage 3 with 512 × 512 images, and the final Stage 4 with 1024 × 1024 images. The outcomes show that the suggested model is effective for analyzing Pap smear tests, achieving 97.4% accuracy and approx. 98% kappa score.
宫颈癌是女性中常见且致命的疾病，其死亡率仅次于乳腺癌，每天有超过 700 人因此死亡。巴氏涂片检查是一种广泛用于早期发现宫颈癌的筛查方法。然而，由于人为错误，这种手动筛查过程容易出现高假阳性结果。研究人员正在使用机器学习和深度学习在计算机辅助诊断工具中解决这个问题。这些工具可以自动分析和分类宫颈细胞学和阴道镜图像，提高识别宫颈癌各个阶段的准确性。
本文使用了最先进的深度学习方法，如 ResNet-50 对宫颈癌细胞进行分类，以帮助医学专业人员。该方法包括三个关键步骤：预处理、使用 k-均值聚类的分割和癌细胞分类。该模型基于性能指标进行评估，如精度、准确性、kappa 评分、精确性、敏感性和特异性。最后，高成功率表明 ResNet50 模型是及时检测宫颈癌的有价值工具。
使用空间 K-均值聚类和预处理操作定位感染的宫颈区域。然后，渐进式学习技术会按顺序进行操作。渐进式学习技术随后经过几个阶段：阶段 1 使用 64×64 图像，阶段 2 使用 224×224 图像，阶段 3 使用 512×512 图像，最后阶段 4 使用 1024×1024 图像。结果表明，所提出的模型可有效用于分析巴氏涂片检查，准确率达 97.4%，kappa 评分约为 98%。
