Deep Learning Approach for Automated Data Augmentation and Multi-class Classification of Pap Smear Images
Sanjana Nayar, D. Lakshmi Priya, Vinitha Panicker J · Procedia Computer Science · 2024
AI is now used to analyze large datasets, which can subsequently be used to predict outcomes and provide patient insights. In this paper, we aim to address the challenge of insufficient medical data, which in turn hinders ML and AI from being used in medicine. By producing additional and related medical data, data augmentation will make it easier to develop machine learning or deep learning models to automate diagnosis. This study aims to utilize Generative Adversarial Networks to create synthetic images and to develop a model to effectively classify the dataset, including the newly generated images, into NILM, HSIL, LSIL, or SCC, which were the four types of cancer cells provided in the dataset. The LBC dataset for multi-class classification has been classified with a state-of-the-art accuracy of 99.1% in our study. The testing accuracy achieved using traditional methods is 98.8%, whereas using GAN is 99.1%, demonstrating that employing synthetic images created using GANs can be a better augmentation method than conventional ways.