CerviXpert: A Multi-Structural Convolutional Neural Network for Predicting Cervix Type and Cervical Cell Abnormalities

Cervical cancer affects millions of women worldwide and has a significantly higher survival rate when diagnosed early. Pap smears and cervical biopsies are vital screening tools for detecting such cancer. However, the success of these screening processes depends on the skills of cytologists. A recen...

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Bibliographic Details
Published inarXiv.org
Main Authors Rashik Shahriar Akash, Islam, Radiful, S M Saiful Islam Badhon, K S M Tozammel Hossain
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 10.09.2024
Subjects
Abnormalities
Artificial neural networks
Cancer
Cervical cancer
Cervix
Classification
Cytology
Machine learning
Medical screening
Neural networks
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Summary:Cervical cancer affects millions of women worldwide and has a significantly higher survival rate when diagnosed early. Pap smears and cervical biopsies are vital screening tools for detecting such cancer. However, the success of these screening processes depends on the skills of cytologists. A recent trend in diagnostic cytology is to apply machine-learning-based models to classify cancer using cell images. These automated models have been shown to perform just as well as, or even better than, expert cytologists. Some notable methods for classifying cervix cancers include ResNet50, VGG16, MobileNetV2, and InceptionV3, based on deep convolutional neural networks (CNN). However, these methods are computationally expensive. We present CerviXpert, a multi-structural Convolutional Neural Network, to identify cervix cancer. We perform extensive experiments on a publicly available dataset, SiPaKMeD, to show the efficacy of our method. CerviXpert presents a promising solution for efficient cervical cancer screening and diagnosis by striking a balance between accuracy and practical feasibility.
ISSN:2331-8422