MobileNet-Light: A Lightweight TCT Image Classification Model for Cervical Cancer
XingWen Pan, Chengzhuan Bao, DeHua Chen · 2023
Cervical cancer is one of the most common malignant tumors in women. Clinical observations have demonstrated that early screening and treatment can prevent, detect, and cure cervical cancer. TCT image detection has been widely used in cervical disease screening, yet the numerous cell counts in TCT image samples make traditional manual diagnostic screening consume a significant amount of human resources. Accurately and rapidly classifying TCT images is essential for cervical cancer screening. This paper proposed a MobileNet-Light model for cervical cancer TCT image classification based on lightweight design. The Ghost module was introduced in the lightweight model design to optimize redundant image features in the network. Moreover, the model design included an efficient channel attention mechanism based on ECA, which improved the classification accuracy while significantly reducing the parameter count. Finally, to address the black-box problem of the classification model, we used the Grad-Cam based interpretable method to provide some interpretability to the model's classification results as diagnostic basis for doctors. Experimental results demonstrated that our lightweight classification model remarkably performs in cervical cancer TCT image classification tasks.