Work

Guarded Diagnosis: Preserving Privacy in Cervical Cancer Detection with Convolutional Neural Networks on Pap Smear Images

Year: 2025

Type: conference-paper

Abstract: Advancements in image processing have advanced medical diagnostics, especially in image classification, impacting healthcare by offering faster and more accurate analyses of magnetic resonance imaging (MRI) and X-rays. The manual examination of these images is slow, error-prone, and costly. Therefore, we propose a new method focusing on the Pap smear exam for early cervical cancer detection. Using a convolutional neural network (CNN) and the SIPaKMeD dataset, cervical cells are classified into normal, precancerous, and benign cells after segmentation. The CNN’s architecture is simple yet efficient, achieving a 91.29% accuracy.

Source: IEEE Eurasia Conference on IOT, Communication and Engineering (ECICE)

DOI: 10.3390/engproc2025092007

Authors: Sanmugasundaram Ravichandran, Hui-Kai Su, Wen-Kai Kuo, Manikandan Mahalingam, Kanimozhi Janarthanan, Kabilan Saravanan, Bruhathi Sathyanarayanan

Institutions: National Formosa University, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Rajalakshmi Engineering College

Language: English

Topic: AI in cancer detection

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: hybrid

OpenAlex ID: W4409507868

FWCI: 0.991

Cites: 14

Cited by: 1

Related to: 10