Attention-enhanced deep learning for cervical cytology: combining convolutional networks with multi-head attention and fuzzy logic

Purpose Cervical cancer continues to be one of the leading causes of death among females worldwide, and thus early diagnosis by using more advanced diagnostic procedures is crucial. The conventional Pap-smear procedure is accurate but subject to human error; thus, computerised, standardised, and automated diagnosis becomes imperative. Herein we present a novel framework of a fuzzy distance-based ensemble of convolutional neural networks (CNNs) for efficient cervical cancer classification from Pap-smear images. Material and methods The proposed approach integrates 5 models of CNN – Simple CNN, InceptionV3, Xception, Xception with Attention, and Inception Attention – via attention mechanisms to advance feature learning. A fuzzy distance-based aggregator function is introduced to fuse the predictions of these models optimally as per Eucli­dean, Manhattan, and cosine distance measures. Four advanced pre-processing techniques – wavelet denoising, contrast-limited adaptive histogram equalisation (CLAHE), background correction, and Laplacian sharpening – are employed to construct a cleaner dataset with enhanced image sharpness and segmentation. Results Experimental outcomes prove that the model is significantly better than state-of-the-art approaches, with an accuracy of 94% on the original dataset and 98.3% on the pre-processed dataset. Conclusions The method suggested herein has better noise robustness, interpretability through fuzzy logic, and automatic adaptation to various CNN frameworks without fine-tuning. These results acknowledge the promise of fuzzy logic-based CNN ensembles to improve machine-based cervical cancer diagnosis, which could be mapped to better and scalable diagnostic instruments in medical imaging.