Equilibrium Optimization Algorithm with Ensemble Learning Based Cervical Precancerous Lesion Classification Model

• Published in: Healthcare (Basel) Vol. 11; no. 1; p. 55
• Main Authors: A. Mansouri, Rasha, Ragab, Mahmoud
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 25.12.2022; MDPI
• Subjects: Algorithms; Artificial intelligence; Automation; Cancer; Cancer cells; Cervical cancer; Classification; Computational linguistics; Datasets; Diagnosis; Equilibrium; Exploitation; Forecasts and trends; Health aspects; Human papillomavirus; Identification and classification; Language processing; Lesions; Machine learning; Mathematical optimization; Natural language interfaces; Neural networks; Pap smear; Risk factors; Saudi Arabia; ensemble learning; cervical cancer; medical imaging; decision making; healthcare
• ISSN: 2227-9032; 2227-9032
• DOI: 10.3390/healthcare11010055

Recently, artificial intelligence (AI) with deep learning (DL) and machine learning (ML) has been extensively used to automate labor-intensive and time-consuming work and to help in prognosis and diagnosis. AI’s role in biomedical and biological imaging is an emerging field of research and reveals future trends. Cervical cell (CCL) classification is crucial in screening cervical cancer (CC) at an earlier stage. Unlike the traditional classification method, which depends on hand-engineered or crafted features, convolution neural network (CNN) usually categorizes CCLs through learned features. Moreover, the latent correlation of images might be disregarded in CNN feature learning and thereby influence the representative capability of the CNN feature. This study develops an equilibrium optimizer with ensemble learning-based cervical precancerous lesion classification on colposcopy images (EOEL-PCLCCI) technique. The presented EOEL-PCLCCI technique mainly focuses on identifying and classifying cervical cancer on colposcopy images. In the presented EOEL-PCLCCI technique, the DenseNet-264 architecture is used for the feature extractor, and the EO algorithm is applied as a hyperparameter optimizer. An ensemble of weighted voting classifications, namely long short-term memory (LSTM) and gated recurrent unit (GRU), is used for the classification process. A widespread simulation analysis is performed on a benchmark dataset to depict the superior performance of the EOEL-PCLCCI approach, and the results demonstrated the betterment of the EOEL-PCLCCI algorithm over other DL models.