Automated real-time cervical cancer diagnosis using NVIDIA Jetson Nano

Abstract

Cervical cancer is a global health concern, making early detection critical for ensuring effective treatment outcomes. Screening technique, the Papanicolaou test (Pap test), has been adopted globally for timely detection. Nevertheless, the process of screening is subjective. The current study aims to advance the development of an automated real time framework for cervical cell analysis for early-stage diagnosis using supervised classification on NVIDIA Jetson Nano platform. Our approach, leveraging adaptive fuzzy k-means (AFKM) clustering and k-means clustering, extracts distinctive features from cervical cell images for accurate classification. Utilizing multilayer perceptron (MLP) and support vector machine (SVM) classifiers, we achieved a classification accuracy of 97%, highlighting the potential of our system for real-time applications in cervical cancer investigation. Validation by two expert pathologists further supports the system’s practical utility.