International Journal of Imaging Systems and Technology, Journal Year: 2025, Volume and Issue: 35(2)

Published: Feb. 18, 2025

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 susceptible to human error. This study introduces innovative approach that employs convolutional neural networks (CNN) enhance accuracy efficiency cervical cell classification. The proposed model incorporates Convolutional Block Attention Module (CBAM) parallel branch architectures, which facilitate feature extraction by focusing on crucial spatial channel information. process entails identification utilization most pertinent elements within image purpose was meticulously assessed SIPaKMeD dataset, attaining exceptional degree (92.82%), surpassed performance CNN models. incorporation sophisticated attention mechanisms enables not only accurately classify images but also interpretability emphasizing regions images. highlights transformative potential cutting‐edge deep learning techniques in medical analysis, particularly screening, providing powerful tool support pathologists accurate diagnosis. Future work will explore additional extend application this architecture other imaging tasks, further enhancing its clinical utility impact

Language: Английский