Modified metaheuristics with stacked sparse denoising autoencoder model for cervical cancer classification

•Present an optimal SSDAE to classify cervical cancer on pap smear images.•Employ Kapur's entropy segmentation and efficientnet feature extraction.•Propose modified firefly optimization algorithm for hyperparameter tuning.•Validate the performance of proposed model on Herlev database. Cervical...

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Vydáno v:Computers & electrical engineering Ročník 103; s. 108292
Hlavní autoři: Vaiyapuri, Thavavel, Alaskar, Haya, Syed, Liyakathunisa, Aljohani, Eman, Alkhayyat, Ahmed, Shankar, K., Kumar, Sachin
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 01.10.2022
Témata:
Cervical cancer
Computer aided diagnosis
Computer vision
Decision making
Deep neural network
Image-guided intervention
Medical imaging
Pap smear images
Computer vision
Deep neural network
Pap smear images
Medical imaging
Decision making
Image-guided intervention
Computer aided diagnosis
Cervical cancer
ISSN:0045-7906, 1879-0755
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Shrnutí:•Present an optimal SSDAE to classify cervical cancer on pap smear images.•Employ Kapur's entropy segmentation and efficientnet feature extraction.•Propose modified firefly optimization algorithm for hyperparameter tuning.•Validate the performance of proposed model on Herlev database. Cervical cancer is the most commonly diagnosed cancer among women globally, with high mortality rate. For early diagnosis, automated and accurate cervical cancer classification approaches can be developed through effective classification of Pap smear cell images. The current study introduces a novel Modified Firefly Optimization Algorithm with Deep Learning-enabled cervical cancer classification (MFFOA-DL3) model for the classification of Pap Smear Images (PSI). The proposed MFFOA-DL3 model examines the PSI for the existence of cervical cancer cells. To accomplish this, the proposed MFFOA-DL3 model primarily applies Bilateral Filtering (BF)-based noise removal approach to get rid of the noise. Then, Kapur's entropy-based image segmentation technique is applied to determine the affected regions. Moreover, EfficientNet technique is also applied to generate the feature vectors. Finally, MFFOA with Stacked Sparse Denoising Autoencoder (SSDA) model is exploited to classify the PSI. In current study, MFFOA is utilized to appropriately modify the parameters related to SSDA model. The proposed MFFOA-DL3 model was experimentally validated using benchmark dataset. The results attained from extensive comparative analysis highlighted the better performance of MFFOA-DL3 model over other recent approaches. [Display omitted]
ISSN:0045-7906
1879-0755
DOI:10.1016/j.compeleceng.2022.108292