Research Article HDFCN: A Robust Hybrid Deep Network Based on Feature Concatenation for Cervical Cancer Diagnosis on WSI Pap Smear Slides Nitin Kumar Chauhan , 1,2 Krishna Singh , 3 Amit Kumar , 2,4 and Swapnil Baburav Kolambakar 5 1 USIC&T, Guru Gobind Singh Indraprastha University, New Delhi 110078, India 2 Department of ECE, Indore Institute of Science & Technology, Indore 453331, India 3 DSEU Okhla Campus-I, Formerly G. B. Pant Engineering College, New Delhi 110020, India 4 Department of Electronics Engineering, Indian Institute of Technology (BHU), Varanasi 221005, India 5 Bluecrest university college, Monrovia, Liberia Correspondence should be addressed to Swapnil Baburav Kolambakar; k.swapnil@bluecrest.edu.lr Received 1 November 2022; Revised 6 January 2023; Accepted 18 March 2023; Published 17 April 2023 Academic Editor: Sami Azam Copyright © 2023 Nitin Kumar Chauhan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Cervical cancer is a critical imperilment to a female’s health due to its malignancy and fatality rate. The disease can be thoroughly cured by locating and treating the infected tissues in the preliminary phase. The traditional practice for screening cervical cancer is the examination of cervix tissues using the Papanicolaou (Pap) test. Manual inspection of pap smears involves false-negative outcomes due to human error even in the presence of the infected sample. Automated computer vision diagnosis revamps this obstacle and plays a substantial role in screening abnormal tissues affected due to cervical cancer. Here, in this paper, we propose a hybrid deep feature concatenated network (HDFCN) following two-step data augmentation to detect cervical cancer for binary and multiclass classification on the Pap smear images. This network carries out the classification of malignant samples for whole slide images (WSI) of the openly accessible SIPaKMeD database by utilizing the concatenation of features extracted from the fine-tuning of the deep learning (DL) models, namely, VGG-16, ResNet-152, and DenseNet-169, pretrained on the ImageNet dataset. The performance outcomes of the proposed model are compared with the individual performances of the aforementioned DL networks using transfer learning (TL). Our proposed model achieved an accuracy of 97.45% and 99.29% for 5-class and 2-class classifications, respectively. Additionally, the experiment is performed to classify liquid-based cytology (LBC) WSI data containing pap smear images. 1. Introduction Cancer is coerced by the obsolete and irregular evolution of cells in the human body. This deformity can infiltrate the nearby cells in that tissue together with the other tissues and may disperse into more body organs. Cervical cancer arises due to the contagion of the human papillomavirus (HPV) which causes an anomaly in the cervix through which the lower portion of the uterus and vagina connect. Cervical cancer was the fourth most genre of cancer in stat- ics of new indices and fatalities following breast, colorectum, and lung cancer in 2020 [1]. The scarcity of screening and therapeutic systems consequences a high mortality rate in low and middle-income economies. The preliminary traits of cervical cancer comprise an erratic feminine cycle, postin- tercourse vagary bleeding, strong vaginal stink with dis- charge, inexplicable and relentless pelvic, intestinal, or back agony, exhaustion, and diminution in weight [2, 3]. An adequate diagnosis can be procured by using the potential preliminary investigation of cervical lesions for the contraction of the mortality rate by cause of cervical can- cer. The most trusted and well-known approach for the Hindawi BioMed Research International Volume 2023, Article ID 4214817, 17 pages https://doi.org/10.1155/2023/4214817