Deep-HTCNN Framework for Automated Cervical Cancer Classification Enhanced with Metaheuristic Approach

Sanat Jain; Ashish Jain; Mahesh Jangid; Rohit Verma

doi:10.1007/978-981-96-6329-3_17

Deep-HTCNN Framework for Automated Cervical Cancer Classification Enhanced with Metaheuristic Approach

Sanat Jain
, Ashish Jain
, Mahesh Jangid
, Rohit Verma

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Every year, millions of women throughout the world are affected by cancer of the cervix. Earlier diagnosis and treatment by optimal medical guidance are essential to mitigate the impacts of the condition similar to other issues. Smear images are among the most effective methods for detecting cervical malignancy. This paper presents a Deep Hyper Tuned Convolutional Neural Network (Deep-HTCNN) utilizing a chaotic theory-based modified grey wolf optimization (CGWO) technique which is suitable for accurate categorization of cervical cancer with smear images. The Grey Wolf Optimizer (GWO) is a nature-inspired meta-heuristic method driven by the social hunting habits of grey wolves. This work incorporates the chaos hypothesis with the GWO algorithm to enhance its global convergence rate. The findings indicated that utilizing a suitable chaotic map enables CGWO to significantly surpass traditional GWO, demonstrating superior performance relative to other frameworks.

Original language	English
Title of host publication	Proceedings of International Conference on Innovations in Data Science - ICIDS 2024
Editors	Mohammad Shorif Uddin, Dharm Singh, Thittaporn Ganokratanaa, Gaurav Kumawat
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	215-225
Number of pages	11
ISBN (Print)	9789819663286
DOIs	https://doi.org/10.1007/978-981-96-6329-3_17
Publication status	Published - 2026
Externally published	Yes
Event	International Conference on Innovations in Data Science, ICIDS 2024 - Jaipur, India Duration: 28 Nov 2024 → 29 Nov 2024

Publication series

Name	Lecture Notes in Networks and Systems
Volume	1367 LNNS
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	International Conference on Innovations in Data Science, ICIDS 2024
Country/Territory	India
City	Jaipur
Period	28/11/24 → 29/11/24

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Convolution neural network
Grey wolf optimization
Hyperparameter
Pap smear images

Access to Document

10.1007/978-981-96-6329-3_17Licence: Unspecified

Cite this

Jain, S., Jain, A., Jangid, M., & Verma, R. (2026). Deep-HTCNN Framework for Automated Cervical Cancer Classification Enhanced with Metaheuristic Approach. In M. S. Uddin, D. Singh, T. Ganokratanaa, & G. Kumawat (Eds.), Proceedings of International Conference on Innovations in Data Science - ICIDS 2024 (pp. 215-225). (Lecture Notes in Networks and Systems; Vol. 1367 LNNS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-96-6329-3_17

Jain, Sanat ; Jain, Ashish ; Jangid, Mahesh et al. / Deep-HTCNN Framework for Automated Cervical Cancer Classification Enhanced with Metaheuristic Approach. Proceedings of International Conference on Innovations in Data Science - ICIDS 2024. editor / Mohammad Shorif Uddin ; Dharm Singh ; Thittaporn Ganokratanaa ; Gaurav Kumawat. Springer Science and Business Media Deutschland GmbH, 2026. pp. 215-225 (Lecture Notes in Networks and Systems).

@inproceedings{9e0ad97013e941c899af987e2bdd1ff8,

title = "Deep-HTCNN Framework for Automated Cervical Cancer Classification Enhanced with Metaheuristic Approach",

abstract = "Every year, millions of women throughout the world are affected by cancer of the cervix. Earlier diagnosis and treatment by optimal medical guidance are essential to mitigate the impacts of the condition similar to other issues. Smear images are among the most effective methods for detecting cervical malignancy. This paper presents a Deep Hyper Tuned Convolutional Neural Network (Deep-HTCNN) utilizing a chaotic theory-based modified grey wolf optimization (CGWO) technique which is suitable for accurate categorization of cervical cancer with smear images. The Grey Wolf Optimizer (GWO) is a nature-inspired meta-heuristic method driven by the social hunting habits of grey wolves. This work incorporates the chaos hypothesis with the GWO algorithm to enhance its global convergence rate. The findings indicated that utilizing a suitable chaotic map enables CGWO to significantly surpass traditional GWO, demonstrating superior performance relative to other frameworks.",

keywords = "Convolution neural network, Grey wolf optimization, Hyperparameter, Pap smear images",

author = "Sanat Jain and Ashish Jain and Mahesh Jangid and Rohit Verma",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.; International Conference on Innovations in Data Science, ICIDS 2024 ; Conference date: 28-11-2024 Through 29-11-2024",

year = "2026",

doi = "10.1007/978-981-96-6329-3\_17",

language = "English",

isbn = "9789819663286",

series = "Lecture Notes in Networks and Systems",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "215--225",

editor = "Uddin, \{Mohammad Shorif\} and Dharm Singh and Thittaporn Ganokratanaa and Gaurav Kumawat",

booktitle = "Proceedings of International Conference on Innovations in Data Science - ICIDS 2024",

address = "Germany",

}

Jain, S, Jain, A, Jangid, M & Verma, R 2026, Deep-HTCNN Framework for Automated Cervical Cancer Classification Enhanced with Metaheuristic Approach. in MS Uddin, D Singh, T Ganokratanaa & G Kumawat (eds), Proceedings of International Conference on Innovations in Data Science - ICIDS 2024. Lecture Notes in Networks and Systems, vol. 1367 LNNS, Springer Science and Business Media Deutschland GmbH, pp. 215-225, International Conference on Innovations in Data Science, ICIDS 2024, Jaipur, India, 28/11/24. https://doi.org/10.1007/978-981-96-6329-3_17

Deep-HTCNN Framework for Automated Cervical Cancer Classification Enhanced with Metaheuristic Approach. / Jain, Sanat; Jain, Ashish; Jangid, Mahesh et al.
Proceedings of International Conference on Innovations in Data Science - ICIDS 2024. ed. / Mohammad Shorif Uddin; Dharm Singh; Thittaporn Ganokratanaa; Gaurav Kumawat. Springer Science and Business Media Deutschland GmbH, 2026. p. 215-225 (Lecture Notes in Networks and Systems; Vol. 1367 LNNS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Deep-HTCNN Framework for Automated Cervical Cancer Classification Enhanced with Metaheuristic Approach

AU - Jain, Sanat

AU - Jain, Ashish

AU - Jangid, Mahesh

AU - Verma, Rohit

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.

PY - 2026

Y1 - 2026

N2 - Every year, millions of women throughout the world are affected by cancer of the cervix. Earlier diagnosis and treatment by optimal medical guidance are essential to mitigate the impacts of the condition similar to other issues. Smear images are among the most effective methods for detecting cervical malignancy. This paper presents a Deep Hyper Tuned Convolutional Neural Network (Deep-HTCNN) utilizing a chaotic theory-based modified grey wolf optimization (CGWO) technique which is suitable for accurate categorization of cervical cancer with smear images. The Grey Wolf Optimizer (GWO) is a nature-inspired meta-heuristic method driven by the social hunting habits of grey wolves. This work incorporates the chaos hypothesis with the GWO algorithm to enhance its global convergence rate. The findings indicated that utilizing a suitable chaotic map enables CGWO to significantly surpass traditional GWO, demonstrating superior performance relative to other frameworks.

AB - Every year, millions of women throughout the world are affected by cancer of the cervix. Earlier diagnosis and treatment by optimal medical guidance are essential to mitigate the impacts of the condition similar to other issues. Smear images are among the most effective methods for detecting cervical malignancy. This paper presents a Deep Hyper Tuned Convolutional Neural Network (Deep-HTCNN) utilizing a chaotic theory-based modified grey wolf optimization (CGWO) technique which is suitable for accurate categorization of cervical cancer with smear images. The Grey Wolf Optimizer (GWO) is a nature-inspired meta-heuristic method driven by the social hunting habits of grey wolves. This work incorporates the chaos hypothesis with the GWO algorithm to enhance its global convergence rate. The findings indicated that utilizing a suitable chaotic map enables CGWO to significantly surpass traditional GWO, demonstrating superior performance relative to other frameworks.

KW - Convolution neural network

KW - Grey wolf optimization

KW - Hyperparameter

KW - Pap smear images

UR - https://www.scopus.com/pages/publications/105027934082

U2 - 10.1007/978-981-96-6329-3_17

DO - 10.1007/978-981-96-6329-3_17

M3 - Conference contribution

AN - SCOPUS:105027934082

SN - 9789819663286

T3 - Lecture Notes in Networks and Systems

SP - 215

EP - 225

BT - Proceedings of International Conference on Innovations in Data Science - ICIDS 2024

A2 - Uddin, Mohammad Shorif

A2 - Singh, Dharm

A2 - Ganokratanaa, Thittaporn

A2 - Kumawat, Gaurav

PB - Springer Science and Business Media Deutschland GmbH

T2 - International Conference on Innovations in Data Science, ICIDS 2024

Y2 - 28 November 2024 through 29 November 2024

ER -

Jain S, Jain A, Jangid M, Verma R. Deep-HTCNN Framework for Automated Cervical Cancer Classification Enhanced with Metaheuristic Approach. In Uddin MS, Singh D, Ganokratanaa T, Kumawat G, editors, Proceedings of International Conference on Innovations in Data Science - ICIDS 2024. Springer Science and Business Media Deutschland GmbH. 2026. p. 215-225. (Lecture Notes in Networks and Systems). doi: 10.1007/978-981-96-6329-3_17

Deep-HTCNN Framework for Automated Cervical Cancer Classification Enhanced with Metaheuristic Approach

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

Fingerprint

Cite this