Authors:
Ferdaous Idlahcen
1
;
Ali Idri
1
;
2
and
Hasnae Zerouaoui
1
Affiliations:
1
Al Khwarizmi College of Computing, Mohammed VI Polytechnic University, 43150 Ben Guerir, Morocco
;
2
Software Project Management Research Team, ENSIAS, Mohammed V University, 10000 Rabat, Morocco
Keyword(s):
Uterine Cervical Neoplasms, Liquid-Based Cervical Cytology (LBCC), Squamous Cell Carcinoma (SCC), Negative for Intraepithelial Lesion or Malignancy (NILM), AI-Assisted Screening, Digital and Computational Pathology (DCP).
Abstract:
Artificial intelligence (AI)-assisted cervical cytology is poised to enhance sensitivity whilst lessening bias, labor, and time expenses. It typically involves image processing and deep learning to automatically recognize pre-cancerous lesions on a given whole-slide image (WSI) prior to lethal invasive cancer development. Here, we introduce autoencoder (AE)-based hybrid models for cervical carcinoma prediction on the Mendeley-liquid-based cytology dataset. This is built on fourteen combinations of AE, DenseNet-201, and six state-of-the-art classifiers: adaptive boosting (AdaBoost), support vector machine (SVM), multilayer perceptron (MLP), decision tree (DT), k-nearest neighbors (k-NN), and random forest (RF). As empirical evaluations, four performance metrics, Scott-Knott (SK), and Borda count voting scheme, were performed. The AE-based hybrid models integrating AdaBoost, MLP, and RF as classifiers are among the top-ranked architectures, with respective accuracy values of 99.30, 99.
20, and 98.48%. Yet, DenseNet-201 remains a solid option when adopting an end-to-end training strategy.
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