A Novel Framework Leveraging Adam Optimization Techniques, Coupled with Mask R-CNN Deep Learning Mechanisms for Cervical Cancer Detection System

Cervical cancer remains a significant public health concern, with early detection crucial for successful treatment. Deep learning techniques offer promising avenues for automated cancer diagnosis, and this work explores the potential of the Mask R-CNN model with Adam optimization for cervical cancer detection in colposcopy images. This study aims to develop and evaluate a deep learning system based on Mask R-CNN and Adam optimization for automated detection and segmentation of cervical lesions in colposcopy images. The proposed system utilizes Mask R-CNN, a deep convolutional neural network (CNN) is capable of both object detection and instance segmentation. Pre-trained on the COCO dataset, the model is fine-tuned on a labeled colposcopy image dataset for cervical lesion detection and segmentation. Adam optimization, an efficient gradient descent algorithm, is used to maximize the training process of the model. High accuracy in recognizing and differentiating cervical lesions in colposcopy images is anticipated from the system. The system should demonstrate its ability to perform effectively on unseen data and handle variations in image quality. The proposed system’s performance is evaluated on a separate test dataset using standard metrics like accuracy, sensitivity, specificity, and precision for both lesion detection and segmentation tasks. Additionally, the system’s generalizability and robustness to variations in image quality are assessed. This novel approach has the potential to significantly contribute to the field of cervical cancer diagnosis by providing an automated, accurate, and efficient system for early detection, ultimately leading to better patient outcomes.