CVM-Cervix: A Hybrid Cervical Pap-Smear Image Classification Framework Using CNN, Visual Transformer and Multilayer Perceptron

no code implementations • 2 Jun 2022 • Wanli Liu, Chen Li, Ning Xu, Tao Jiang, Md Mamunur Rahaman, Hongzan Sun, Xiangchen Wu, Weiming Hu, HaoYuan Chen, Changhao Sun, YuDong Yao, Marcin Grzegorzek

Cervical cytopathology image classification is an important method to diagnose cervical cancer.

Classification Image Classification

Is the aspect ratio of cells important in deep learning? A robust comparison of deep learning methods for multi-scale cytopathology cell image classification: from convolutional neural networks to visual transformers

no code implementations • 16 May 2021 • Wanli Liu, Chen Li, Md Mamunur Rahamana, Tao Jiang, Hongzan Sun, Xiangchen Wu, Weiming Hu, HaoYuan Chen, Changhao Sun, YuDong Yao, Marcin Grzegorzek

The results of the study indicate that deep learning models are robust to changes in the aspect ratio of cells in cervical cytopathological images.

Image Classification Robust classification

DeepCervix: A Deep Learning-based Framework for the Classification of Cervical Cells Using Hybrid Deep Feature Fusion Techniques

no code implementations • 24 Feb 2021 • Md Mamunur Rahaman, Chen Li, YuDong Yao, Frank Kulwa, Xiangchen Wu, Xiaoyan Li, Qian Wang

Pap smear test is a widely performed screening technique for early detection of cervical cancer, whereas this manual screening method suffers from high false-positive results because of human errors.

Cell Segmentation Classification +1