A federated learning aided system for classifying cervical cancer using PAP-SMEAR images

Abstract

Cervical cancer is a significant contributor to female mortality on a global scale, especially in low-income countries where effective screening programs for the detection and treatment of precancerous conditions are lacking. Classification of pap-smear test cervical cell images is crucial as it gives essential information for the diagnosis of malignant or precancerous lesions and thus helps in providing a proper diagnosis. Most of the existing methods require accumulating pap-smear test images of all patients in a centralized location for classification purposes. However, this procedure may hamper the privacy of patient data and create data ownership issues. In this study, different convolutional neural network-based federated learning architectures are introduced to achieve both the objectives of accurate image classification and data privacy in three different experimental settings. In the proposed system, the updates of the locally trained models get aggregated with an initially untrained global model in order to increase its performance. In traditional ML-based systems, the more the train data, the more efficiently the model performs, but in the proposed system, clients can participate remotely to train a robust model even with the disadvantage of possessing a small dataset. The proposed CNN-based FL architecture showed test accuracy of 94.36% and 78.4% in an IID (Independent and Identically Distributed) and a non-IID setting respectively. Thus multiple hospitals across different countries can use the proposed system to train their local models with their private dataset without sharing it centrally, which eventually will help to build the central model of federated learning architecture with diverse datasets.