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CerviXpert: A multi-structural convolutional neural network for predicting cervix type and cervical cell

Rashik Shahriar Akash¹, Radiful Islam¹, Sm Saiful Islam Badhon²

¹Department of Computer Science and Engineering, Daffodil International University, Dhaka, Bangladesh.

|November 12, 2024

Summary

CerviXpert, a new AI tool, accurately detects cervical cancer cell abnormalities and classifies cervix types. This efficient model offers a promising solution for early cervical cancer screening, especially in resource-limited settings.

Keywords:

Cervical cancer cervix cell types computer-aided diagnostics diagnostic cytology multi-structural convolutional neural network

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Area of Science:

Medical Imaging
Artificial Intelligence in Healthcare
Oncology

Background:

Cervical cancer is a major global health concern, with early detection significantly improving survival rates.
Current diagnostic methods like Pap smears and biopsies are operator-dependent and prone to errors.
There is a need for efficient and accurate automated tools for cervical cancer screening.

Purpose of the Study:

To develop CerviXpert, a novel multi-structural convolutional neural network for classifying cervix types and detecting cervical cell abnormalities.
To evaluate the accuracy and computational efficiency of CerviXpert compared to existing state-of-the-art models.

Main Methods:

CerviXpert, a computationally efficient convolutional neural network, was developed using the SiPaKMeD dataset.
The model architecture features a simplified design with limited convolutional layers, max-pooling, and dense layers, trained from scratch.
Performance was assessed using five-fold cross-validation, comparing CerviXpert against ResNet50, VGG16, MobileNetV2, and InceptionV3.

Main Results:

CerviXpert achieved 98.04% accuracy in classifying cervical cell abnormalities (normal, abnormal, benign) and 98.60% for five-class cervix type classification.
The model outperformed MobileNetV2 and InceptionV3 in both accuracy and computational demands.
CerviXpert demonstrated comparable accuracy to ResNet50 and VGG16 but with significantly reduced computational complexity and resource usage.

Conclusions:

CerviXpert presents a promising, accurate, and computationally feasible solution for cervical cancer screening and diagnosis.
Its streamlined architecture is suitable for deployment in resource-constrained environments, potentially enhancing early detection and management of cervical cancer.