Cervical precancerous lesion classification using quantum invasive weed optimization with deep learning on biomedical pap smear images

• Published in: Expert systems 2024-07, Vol.41 (7), p.n/a
• Main Authors: Mishra, Awanish Kumar, Gupta, Indresh Kumar, Diwan, Tarun Dhar, Srivastava, Swati
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial intelligence; Artificial neural networks; biomedical imaging; Cancer; Cervical cancer; Classification; Deep learning; Feature extraction; Image enhancement; Image filters; Lesions; Medical imaging; Metabolites; metaheuristics; Optimization; Pap smear; pap smear images
• ISSN: 0266-4720; 1468-0394
• DOI: 10.1111/exsy.13308

Biomedical imaging devices, in general, have been made and used a lot lately to examine the insides of the body during diagnostic and analytic procedures. Biomedical imaging gives accurate information about metabolites, which can be used to find and classify diseases because it is not invasive. For the study of cervical cancer (CC), the pap smear is a crucial type of biological imaging. CC is a crucial reason to enhance the rate of women's mortalities. Proper screening of pap smear images is critical for assisting in the early detection and analysis of CC. Computer‐aided systems for cancerous cell recognition need well established artificial intelligence (AI) methods. In this study, we introduce an automated Cervical Precancerous Lesion Classification using Quantum Invasive Weed Optimization with Deep Learning (CPLC‐QIWODL) on biomedical pap smear images. The presented CPLC‐QIWODL technique examines the pap smear images for cervical cancer classification. To do so, the presented CPLC‐QIWODL technique pre‐processes the biomedical images using a Gabor filtering (GF) approach. Moreover, the CPLC‐QIWODL technique uses a deep convolutional neural network‐based SqueezeNet system for feature extraction. Furthermore, the hyperparameter tuning of the SqueezeNet methodology takes place using the QIWO technique, showing the novelty of the work. Finally, to classify CC, the deep variational autoencoder (DVAE) model is applied. The experimental result analysis of the CPLC‐QIWODL technique is tested using a benchmark medical image database. Extensive comparative results demonstrated the enhanced outcomes of the CPLC‐QIWODL technique over other existing algorithms, with a maximum accuracy of 99.07%.