An automated and Lightweight Recursive Kernel Optimized Network (LReKON) learning model for cervical cancer diagnosis

Cervical cancer is the most prevalent long-term illnesses that can be highly affected women around the world. Images from Pap smears are a widely used technology for cervical cancer screening and diagnosis. Even when the contaminated sample is present, human error can lead to false-negative results when examining pap smears. This challenge has been revamped by automated image processing diagnostics, which is crucial in identifying abnormal tissues impacted by cervical cancer. Therefore, the proposed study aims to develop an automated and lightweight cervical cancer diagnosis system, known as, Lightweight Recursive Kernel Optimized Network (LReKON) for fast and accurate cervical cancer diagnosis. The aberrant region has been more accurately segmented from the raw cervical pictures using the Squeeze and Excitation Instance Segmentation Network (SEI-SN) technology. A novel algorithm termed Optimal Hyperplane based Kernel Neural Network (OHyKN) is used to determine the segmented region as either healthy or cancer-affected, depending on the relevant class. This work also employs a novel Hybrid Heap based Diffusion Vector Optimizer (H²DVO) technique to enhance the training and testing performance of the classifier and expedite the prediction process. Additionally, the proposed LReKON model's segmentation and classification performance is tested and verified using publicly available benchmarking datasets, including the Mendeley LBC and the SIPaKMed Pap smear image dataset, taking into account a number of factors. The trained LReKON model is best in cervical cancer diagnosis with 99.10 % accuracy, 99 % precision, 98.9 % recall, and 98.9 % F1-score and with an extremely fast inference time of 0.28 s. The SEI-SN segmentation module plays a crucial role in performance enhancement as its removal reduces accuracy to 95.15 %, and the removal of the OHyKN classification module reduces accuracy to 96.25 %. The H2DVO optimization step improves efficiency since its elimination results in an additional inference time of 0.32 s and accuracy of 97.35 %. Moreover, when the three components SEI-SN, OHyKN, and H2DVO are eliminated, accuracy reduces to 93.55 %, defining their combined contribution to segmentation, classification, and optimization.