Comparative Performance Analysis of Deep Learning Models in Cervical Cancer Detection

Title

Comparative Performance Analysis of Deep Learning Models in Cervical Cancer Detection

Creator

Mukku L.; Thomas J.

Description

Cervical cancer one of the four most common malignancies worldwide and poses a significant threat, particularly in resource-constrained regions. Automated diagnostic approaches, leveraging colposcope image analysis, hold great promise in curbing the impact of this disease. In this paper, we deploy a range of deep learning methods, including DenseNet 121, ResNet 50, AlexNet and VGG 16 to classify the cervical intraepithelial neoplasia. Our methodology is deployed on a dataset sourced from a Cancer Research institute in India. The current experiment aims to establish the execution of the state-of-the-art pretrained frameworks in deep learning. This will be a baseline experiment for researcher who aim to develop further deep learning models for cervical cancer diagnosis. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

Source

Lecture Notes in Networks and Systems, Vol-1046 LNNS, pp. 185-194.

Date

2024-01-01

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

AlexNet; cervical cancer; Deep learning; DenseNet121; feature integration; ResNet50; VGG16

Coverage

Mukku L., CHRIST (Deemed to Be University), Bangalore, India; Thomas J., CHRIST (Deemed to Be University), Bangalore, India

Rights

Restricted Access

Relation

ISSN: 23673370; ISBN: 978-303164812-0

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1007/978-3-031-64813-7_20

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85200687527&doi=10.1007%2f978-3-031-64813-7_20&partnerID=40&md5=a735c134e330a3e680a3e05dd9256d81