Convolutional Block Attention Module and Parallel Branch Architectures for Cervical Cell Classification

Abstract

Cervical cancer persists as a significant global health concern, underscoring the vital importance of early detection for effective treatment and enhanced patient outcomes. While traditional Pap smear tests remain an invaluable diagnostic tool, they are inherently time-consuming and susceptible to human error. This study introduces an innovative approach that employs convolutional neural networks (CNN) to enhance the accuracy and efficiency of cervical cell classification. The proposed model incorporates the Convolutional Block Attention Module (CBAM) and parallel branch architectures, which facilitate enhanced feature extraction by focusing on crucial spatial and channel information. The process of feature extraction entails the identification and utilization of the most pertinent elements within an image for the purpose of classification. The proposed model was meticulously assessed on the SIPaKMeD dataset, attaining an exceptional degree of accuracy (92.82%), which surpassed the performance of traditional CNN models. The incorporation of sophisticated attention mechanisms enables the model to not only accurately classify images but also facilitate interpretability by emphasizing crucial regions within the images. This study highlights the transformative potential of cutting-edge deep learning techniques in medical image analysis, particularly for cervical cancer screening, providing a powerful tool to support pathologists in early detection and accurate diagnosis. Future work will explore additional attention mechanisms and extend the application of this architecture to other medical imaging tasks, further enhancing its clinical utility and impact on patient outcomes.