A study on Deep Convolutional Neural Networks, transfer learning, and Mnet model for Cervical Cancer Detection
Saifuddin Sagor, Md Taimur Ahad, Faruk Ahmed, Rokonozzaman Ayon, Sanzida Parvin
通过巴氏涂片分析进行早期和准确的检测对于改善患者预后和降低宫颈癌死亡率至关重要。 最先进的(SOTA)卷积神经网络(CNN)需要大量的计算资源,延长训练时间和大型数据集。 在这项研究中,一个轻量级的CNN模型S-Net(Simple Net)是专门为宫颈癌检测和分类开发的,使用巴氏涂片图像来解决这些限制。 除了S-Net之外,还使用迁移学习评估了六个SOTA CNN,包括多路径(DenseNet201,ResNet152),基于深度(Serasnet152),基于宽度的多连接(Xception),深度可分离卷积(MobileNetV2)和基于空间利用(VGG19)。 所有型号,包括S-Net,都实现了可比的精度,S-Net达到99.99
Early and accurate detection through Pap smear analysis is critical to improving patient outcomes and reducing mortality of Cervical cancer. State-of-the-art (SOTA) Convolutional Neural Networks (CNNs) require substantial computational resources, extended training time, and large datasets. In this study, a lightweight CNN model, S-Net (Simple Net), is developed specifically for cervical cancer detection and classification using Pap smear images to address these limitations. Alongside S-Net, six ...