Deep Dive: Relative Analysis of Cutting-Edge Deep Learning Models for Cervical Cancer Detection
Chetna Vaid Kwatra, Harpreet Kaur · 2023
Often referred to as one of the most life-threatening forms of cancer that has garnered considerable attention in the medical community. A disease that is affecting women’s reproductive systems, accounting for the fourth most occurring cancer in women overall. To ensure accurate cervical cancer prediction and minimize the risk of misdiagnosis, it is essential to employ a comprehensive approach that leverages various deep learning algorithms on a dataset sourced from whole slide images of patients. A total of 6380 images were employed to apprehend the performance of 4 deep learning models in terms of various performance matrices. Our study has uncovered promising findings regarding the application of MobileNetV3, ResNet50, EfficientNet-B3 and InceptionV3 in the realm of cervical cancer identification. The comparative analysis results emphasize the potential of leveraging deep learning models to boost the precision and the effectiveness in detection of cervical cancer, a pivotal step in enhancing patient outcomes, and a valuable resource for healthcare practitioners and researchers.