DDUSeg-Net as a Design of Convolutional Neural Network Architecture for Semantic Segmentation in Cervical Cancer
International journal of intelligent engineering and systems · 2024
Cervical cancer is a significant health issue for women and ranks fourth in the world among the most dangerous cancers.An automatic diagnostic system is needed for pap smears to assist medical experts in diagnosing cervical cancer.One of the automatic diagnosis systems in detecting cervical cancer is semantic segmentation.Convolutional Neural Networks (CNN), particularly the U-Net architecture, have been widely used for segmentation tasks in medical imaging.Although U-Net has demonstrated effectiveness, its performance on low-quality images is often suboptimal, with issues such as loss of fine details during the down-sampling process.This study combines image enhancement and Double Dropout USeg-Net (DDUSeg-Net).Image enhancement techniques are applied to pap-smear images to improve image quality such as Gamma Correction for enhanced contrast, and Median Filtering for reduced noise.The proposed DDUSeg-Net architecture builds on the U-Net model by incorporating two U-Net blocks for more detailed feature extraction.SegNet's pooling indices are added to preserve spatial information during the segmentation process.Additionally, dropout layers are introduced to prevent overfitting and reduce the model's overall complexity.The image enhancement results indicate that the Mean Squared Error (MSE), Peak Signal to Ratio (PNSR), and Structural Similarity Image Index (SSIM) are above 85%.The performance metrics for the DDUSeg-Net model obtained accuracy, precision, recall, and F1-score above 90%.This analysis used 2D pap-smear images from the Herlev dataset.Overall, the combination of image enhancement and DDUSegNet demonstrates strong robustness in the segmentation of pap-smear images, effectively balancing the detection of the intersection areas between the nucleus, cytoplasm, and background.