Advancements in Early Detection of Cervical Cancer using Machine Learning and Deep Learning Models for Cervicography Analysis

This study looks into the efficacy of machine learning (ML) models for early diagnosis of cervical cancer utilizing cervical imaging. They were meticulously built using a dataset of 2322 photos from hospitals and online archives. Following preprocessing and grayscale modification to separate cancer areas, features such as intensity, entropy, cluster shadow, grayscale variance, size, shape, and intensity were retrieved. Three machine learning models (ML) were trained on the retrieved features: Convolutional neural networks (CNN), VGG 16, and Recurrent neural network (RNN). The dataset was separated into 70% testing and 30% training, which produced reliable findings. The CNN model performed the best, with an accuracy of 98.77%, followed by VGG 16 (94.65%) and RNN (87.65%). These models also displayed great sensitivity and specificity, validating their ability to effectively identify malignant and nonmalignant cervical lesions. When trials are compared to existing studies, significant improvements are regularly observed, highlighting the potential of machine learning to revolutionize cervical cancer research beyond what has previously been developed. This work represents a big step forward in the use of computer intelligence for early identification, enabling prompt therapies, and improving outcomes in cervical cancer patients. The findings highlight the impact of ML techniques in increasing changes in diagnostic accuracy and accessibility, and present interesting possibilities for further research and practical application.