Macancela, C.; Morocho-Cayamcela, M.E.; Chang, O. Deep Reinforcement Learning for Efficient Digital Pap Smear Analysis. Computation2023, 11, 252.
Macancela, C.; Morocho-Cayamcela, M.E.; Chang, O. Deep Reinforcement Learning for Efficient Digital Pap Smear Analysis. Computation 2023, 11, 252.
Macancela, C.; Morocho-Cayamcela, M.E.; Chang, O. Deep Reinforcement Learning for Efficient Digital Pap Smear Analysis. Computation2023, 11, 252.
Macancela, C.; Morocho-Cayamcela, M.E.; Chang, O. Deep Reinforcement Learning for Efficient Digital Pap Smear Analysis. Computation 2023, 11, 252.
Abstract
In August 2020, the World Health Assembly launched a global initiative to eliminate cervical cancer by 2030, setting three primary targets. One key goal is to achieve a 70% screening coverage rate for cervical cancer, primarily relying on the precise analysis of Papanicolaou (Pap) or digital Pap smears. However, the responsibility of reviewing Pap smear samples to identify potentially cancerous cells primarily falls on pathologists, a task known to be exceptionally challenging and time-consuming. This paper proposes a solution to address the shortage of pathologists for cervical cancer screening. It leverages the OpenAI-GYM API to create a deep reinforcement learning environment utilizing liquid-based Pap smear images. By employing the Proximal Policy Optimization algorithm, autonomous agents navigate Pap smear images, identifying cells with the aid of rewards, penalties, and accumulated experiences. Furthermore, the use of a pre-trained convolutional neuronal network like Res-Net54 enhances the classification of detected cells based on their potential for malignancy. The ultimate goal of this study is to develop a highly efficient, automated Papanicolaou analysis system, ultimately reducing the need for human intervention in regions with limited pathologists.
Computer Science and Mathematics, Computer Vision and Graphics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
