A Deep Learning-Based Approach for Cervical Cancer Classification Using 3D CNN and Vision Transformer.
Cervical cancer is a significant health problem worldwide, and early detection and treatment are critical to improving patient outcomes. To address this challenge, a deep learning (DL)-based cervical classification system is proposed using 3D convolutional neural network and Vision Transformer (ViT)...
| Published in: | Journal of Digital Imaging Vol. 37; no. 1; pp. 280 - 297 |
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| Main Authors: | , |
| Format: | diagnostic images equations & formulas review tables/charts Journal Article |
| Published: |
Springer Nature
Feb2024
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=175966504&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 175966504 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Feb2024 vid: 37 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 175966504 175966504 175966504 10.1007/s10278-023-00911-z 175966504 ppf: 280 ppct: 17 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A Deep Learning-Based Approach for Cervical Cancer Classification Using 3D CNN and Vision Transformer. aug: au: K., Abinaya B., Sivakumar affil: https://ror.org/050113w36 Department of Computing Technologies, SRM Institute of Science and Technology, Kattankulathur, Chennai, India sug: subj: Deep Learning Cervix Neoplasms Classification Imaging, Three-Dimensional Early Detection of Cancer Cervix Neoplasms Diagnosis Convolutional Neural Networks Diagnostic Errors Prevention and Control Automation Cancer Screening Extreme Learning Machines ab: Cervical cancer is a significant health problem worldwide, and early detection and treatment are critical to improving patient outcomes. To address this challenge, a deep learning (DL)-based cervical classification system is proposed using 3D convolutional neural network and Vision Transformer (ViT) module. The proposed model leverages the capability of 3D CNN to extract spatiotemporal features from cervical images and employs the ViT model to capture and learn complex feature representations. The model consists of an input layer that receives cervical images, followed by a 3D convolution block, which extracts features from the images. The feature maps generated are down-sampled using max-pooling block to eliminate redundant information and preserve important features. Four Vision Transformer models are employed to extract efficient feature maps of different levels of abstraction. The output of each Vision Transformer model is an efficient set of feature maps that captures spatiotemporal information at a specific level of abstraction. The feature maps generated by the Vision Transformer models are then supplied into the 3D feature pyramid network (FPN) module for feature concatenation. The 3D squeeze-and-excitation (SE) block is employed to obtain efficient feature maps that recalibrate the feature responses of the network based on the interdependencies between different feature maps, thereby improving the discriminative power of the model. At last, dimension minimization of feature maps is executed using 3D average pooling layer. Its output is then fed into a kernel extreme learning machine (KELM) for classification into one of the five classes. The KELM uses radial basis kernel function (RBF) for mapping features in high-dimensional feature space and classifying the input samples. The superiority of the proposed model is known using simulation results, achieving an accuracy of 98.6%, demonstrating its potential as an effective tool for cervical cancer classification. Also, it can be used as a diagnostic supportive tool to assist medical experts in accurately identifying cervical cancer in patients. pubtype: Academic Journal doctype: diagnostic images equations & formulas review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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