ADVANCING CERVICAL CANCER DETECTION AND EARLY DIAGNOSIS WITH RESNEXT-BASED TRANSFER LEARNING

Cervical cancer is the fourth leading cause of cancer-related deaths among women worldwide, with mortality rates projected to rise significantly by 2030. Despite being highly preventable and treatable when detected in early stage. Previous studies have compared DL-based models with conventional screening methods; however, their performance in detecting early-stage cervical cancer has been poor. This work introduces a new DL-based method for cervical cancer detection using a colposcopy dataset, which involves three simple stages: image enhancement, feature selection, and classification. Image enhancement methods are first used to improve the quality of colposcopy images, thus making feature extraction better, followed by feature selection using simulated annealing to identify the most relevant features for classification. The features are then used to train a ResXNet classifier, achieving an accuracy of 98%. The proposed model is compared with state-of-the-art deep learning models, including VGG16 and ResNet-50, the results demonstrate that the proposed model as achieved improved performance in terms of accuracy and efficiency.