Authors:
Ferdaous Idlahcen
1
;
Pierjos Francis Colere Mboukou
1
;
Hasnae Zerouaoui
1
and
Ali Idri
1
;
2
Affiliations:
1
Modeling, Simulation, & Data Analysis -MSDA, Mohammed VI Polytechnic University -UM6P, Ben Guerir 43150, Morocco
;
2
Software Project Management Research Team, ENSIAS, Mohammed V University -UM5, Rabat 10000, Morocco
Keyword(s):
Uterine Cervical Neoplasms, Whole-Slide Imaging (WSI), Digital Pathology (DP), Transfer Learning (TL), Computer-aided Detection (CADe) and Diagnosis (CADx).
Abstract:
Cervical cancer (CxCa) is heavily swerved toward low- and middle- income countries (LMICs). Without prompt actions, the burden is anticipated to worsen by 50% from 2020 to 2040 - nearly 90% of deaths to occur in sub-Saharan Africa (SSA). Yet, uterine cervix neoplasms are readily avoidable due to a protracted latent cancer period. As it stands, deep learning (DL) is a potent solution for enhancing the early detection of cervical cancer. This work assesses and compares the performance of seven end-to-end learning architectures to automatically recognize cervical lesions and carcinoma histotypes upon hematoxylin and eosin (H&E)-stained pathology images. Pre-trained VGG16, VGG19, InceptionV3, ResNet50, MobileNetV2, InceptionResNetV2, and DenseNet201 were the implemented deep convolutional neural networks (dCNNs) throughout the present empirical analysis. Experiments are conducted on two datasets: (i) Mendeley liquid-based cytology (LBC) and (ii) The Cancer Genome Atlas (TCGA) Cervical Sq
uamous Cell Carcinoma and Endocervical Adenocarcinoma diagnostic slides. All tests were validated under a 5-fold cross-validation, with four key metrics, Scott-Knott (SK), and Borda count schemes. Both pathology data appear to promote InceptionV3 and DenseNet201. Yet, while VGG16 is a weak-performing approach for liquid-based cytology, it evinces promise in histopathology yielding 99.33% accuracy, 98.85% precision, 99.83% recall, and 99.34% F-measure.
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