College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia.
Department of Computer Engineering and Networks, College of Computer and Information Sciences, Jouf University, Sakaka 72388, Saudi Arabia.
Comput Math Methods Med. 2023 Jul 8;2023:9676206. doi: 10.1155/2023/9676206. eCollection 2023.
Image processing has enabled faster and more accurate image classification. It has been of great benefit to the health industry. Manually examining medical images like MRI and X-rays can be very time-consuming, more prone to human error, and way more costly. One such examination is the Pap smear exam, where the cervical cells are examined in laboratory settings to distinguish healthy cervical cells from abnormal cells, thus indicating early signs of cervical cancer. In this paper, we propose a convolutional neural network- (CNN-) based cervical cell classification using the publicly available SIPaKMeD dataset having five cell categories: superficial-intermediate, parabasal, koilocytotic, metaplastic, and dyskeratotic. CNN distinguishes between healthy cervical cells, cells with precancerous abnormalities, and benign cells. Pap smear images were segmented, and a deep CNN using four convolutional layers was applied to the augmented images of cervical cells obtained from Pap smear slides. A simple yet efficient CNN is proposed that yields an accuracy of 0.9113% and can be successfully used to classify cervical cells. A simple architecture that yields a reasonably good accuracy can increase the speed of diagnosis and decrease the response time, reducing the computation cost. Future researchers can build upon this model to improve the model's accuracy to get a faster and more accurate prediction.
图像处理使得图像分类更快、更准确。它极大地有益于医疗保健行业。人工检查 MRI 和 X 光等医学图像非常耗时，更容易出错，成本也更高。巴氏涂片检查就是一个例子，在实验室环境中检查宫颈细胞，以区分健康的宫颈细胞和异常细胞，从而早期发现宫颈癌。在本文中，我们提出了一种基于卷积神经网络（CNN）的宫颈细胞分类方法，使用了公开的 SIPaKMeD 数据集，该数据集有五个细胞类别：表层-中层、副基底、挖空细胞、化生和角化不良。CNN 可以区分健康的宫颈细胞、有癌前异常的细胞和良性细胞。巴氏涂片图像被分割，然后将一个具有四个卷积层的深度 CNN 应用于从巴氏涂片玻片获得的宫颈细胞增强图像。我们提出了一个简单而有效的 CNN，其准确率为 0.9113%，可成功用于分类宫颈细胞。这种简单的架构可以提高准确性，从而提高诊断速度，减少响应时间，降低计算成本。未来的研究人员可以在此模型的基础上进行改进，以提高模型的准确性，从而实现更快、更准确的预测。
