Multiscale-FocusNet: A Novel Deep Learning Approach for Skin Cancer Detection Using Dermoscopic Images

Shambhavi Sinha; Manan Bhatt; Anu Shaju Areeckal

doi:10.1007/978-981-96-3355-5_30

Multiscale-FocusNet: A Novel Deep Learning Approach for Skin Cancer Detection Using Dermoscopic Images

Shambhavi Sinha
, Manan Bhatt
, Anu Shaju Areeckal^*

^*Corresponding author for this work

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

Abstract

Skin cancer is one of the leading types of cancer in the world, affecting a large part of the population globally. There is a need to develop a precise classification technique to detect benign and malignant tumors non-invasively from dermoscopic images. This paper proposes a novel lightweight deep learning model, Multiscale-FocusNet, for improved diagnosis of skin cancer. This work integrates advanced preprocessing and color consistency techniques with a novel dual attention model built on modified MobileNet-V2 architecture. The proposed method is validated on the public ISIC Challenge dataset. The proposed Multiscale-FocusNet exhibits exceptional performance, achieving 98.85% accuracy, 99.28% precision, 98.21% recall, and 98.74% F1-score. The Multiscale-FocusNet shows a superior promising performance as compared to existing transfer learning models. The proposed method can be effectively used for precise detection of skin cancer using dermoscopic images.

Original language	English
Title of host publication	Proceedings of Data Analytics and Management, ICDAM 2024
Editors	Abhishek Swaroop, Bal Virdee, Sérgio Duarte Correia, Zdzislaw Polkowski
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	377-389
Number of pages	13
ISBN (Print)	9789819633548
DOIs	https://doi.org/10.1007/978-981-96-3355-5_30
Publication status	Published - 2025
Event	5th International Conference on Data Analytics and Management, ICDAM 2024 - London, United Kingdom Duration: 14-06-2024 → 15-06-2024

Publication series

Name	Lecture Notes in Networks and Systems
Volume	1298 LNNS
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	5th International Conference on Data Analytics and Management, ICDAM 2024
Country/Territory	United Kingdom
City	London
Period	14-06-24 → 15-06-24

UN SDGs

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

SDG 3 Good Health and Well-being

All Science Journal Classification (ASJC) codes

Control and Systems Engineering
Signal Processing
Computer Networks and Communications

Access to Document

10.1007/978-981-96-3355-5_30

Cite this

Sinha, S., Bhatt, M., & Areeckal, A. S. (2025). Multiscale-FocusNet: A Novel Deep Learning Approach for Skin Cancer Detection Using Dermoscopic Images. In A. Swaroop, B. Virdee, S. D. Correia, & Z. Polkowski (Eds.), Proceedings of Data Analytics and Management, ICDAM 2024 (pp. 377-389). (Lecture Notes in Networks and Systems; Vol. 1298 LNNS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-96-3355-5_30

Sinha, Shambhavi ; Bhatt, Manan ; Areeckal, Anu Shaju. / Multiscale-FocusNet : A Novel Deep Learning Approach for Skin Cancer Detection Using Dermoscopic Images. Proceedings of Data Analytics and Management, ICDAM 2024. editor / Abhishek Swaroop ; Bal Virdee ; Sérgio Duarte Correia ; Zdzislaw Polkowski. Springer Science and Business Media Deutschland GmbH, 2025. pp. 377-389 (Lecture Notes in Networks and Systems).

@inproceedings{118769d766b84a4d87f7e9df89b8e056,

title = "Multiscale-FocusNet: A Novel Deep Learning Approach for Skin Cancer Detection Using Dermoscopic Images",

abstract = "Skin cancer is one of the leading types of cancer in the world, affecting a large part of the population globally. There is a need to develop a precise classification technique to detect benign and malignant tumors non-invasively from dermoscopic images. This paper proposes a novel lightweight deep learning model, Multiscale-FocusNet, for improved diagnosis of skin cancer. This work integrates advanced preprocessing and color consistency techniques with a novel dual attention model built on modified MobileNet-V2 architecture. The proposed method is validated on the public ISIC Challenge dataset. The proposed Multiscale-FocusNet exhibits exceptional performance, achieving 98.85\% accuracy, 99.28\% precision, 98.21\% recall, and 98.74\% F1-score. The Multiscale-FocusNet shows a superior promising performance as compared to existing transfer learning models. The proposed method can be effectively used for precise detection of skin cancer using dermoscopic images.",

author = "Shambhavi Sinha and Manan Bhatt and Areeckal, \{Anu Shaju\}",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.; 5th International Conference on Data Analytics and Management, ICDAM 2024 ; Conference date: 14-06-2024 Through 15-06-2024",

year = "2025",

doi = "10.1007/978-981-96-3355-5\_30",

language = "English",

isbn = "9789819633548",

series = "Lecture Notes in Networks and Systems",

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

pages = "377--389",

editor = "Abhishek Swaroop and Bal Virdee and Correia, \{S{\'e}rgio Duarte\} and Zdzislaw Polkowski",

booktitle = "Proceedings of Data Analytics and Management, ICDAM 2024",

address = "Germany",

}

Sinha, S, Bhatt, M & Areeckal, AS 2025, Multiscale-FocusNet: A Novel Deep Learning Approach for Skin Cancer Detection Using Dermoscopic Images. in A Swaroop, B Virdee, SD Correia & Z Polkowski (eds), Proceedings of Data Analytics and Management, ICDAM 2024. Lecture Notes in Networks and Systems, vol. 1298 LNNS, Springer Science and Business Media Deutschland GmbH, pp. 377-389, 5th International Conference on Data Analytics and Management, ICDAM 2024, London, United Kingdom, 14-06-24. https://doi.org/10.1007/978-981-96-3355-5_30

Multiscale-FocusNet: A Novel Deep Learning Approach for Skin Cancer Detection Using Dermoscopic Images. / Sinha, Shambhavi; Bhatt, Manan; Areeckal, Anu Shaju.
Proceedings of Data Analytics and Management, ICDAM 2024. ed. / Abhishek Swaroop; Bal Virdee; Sérgio Duarte Correia; Zdzislaw Polkowski. Springer Science and Business Media Deutschland GmbH, 2025. p. 377-389 (Lecture Notes in Networks and Systems; Vol. 1298 LNNS).

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

TY - GEN

T1 - Multiscale-FocusNet

T2 - 5th International Conference on Data Analytics and Management, ICDAM 2024

AU - Sinha, Shambhavi

AU - Bhatt, Manan

AU - Areeckal, Anu Shaju

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

PY - 2025

Y1 - 2025

N2 - Skin cancer is one of the leading types of cancer in the world, affecting a large part of the population globally. There is a need to develop a precise classification technique to detect benign and malignant tumors non-invasively from dermoscopic images. This paper proposes a novel lightweight deep learning model, Multiscale-FocusNet, for improved diagnosis of skin cancer. This work integrates advanced preprocessing and color consistency techniques with a novel dual attention model built on modified MobileNet-V2 architecture. The proposed method is validated on the public ISIC Challenge dataset. The proposed Multiscale-FocusNet exhibits exceptional performance, achieving 98.85% accuracy, 99.28% precision, 98.21% recall, and 98.74% F1-score. The Multiscale-FocusNet shows a superior promising performance as compared to existing transfer learning models. The proposed method can be effectively used for precise detection of skin cancer using dermoscopic images.

AB - Skin cancer is one of the leading types of cancer in the world, affecting a large part of the population globally. There is a need to develop a precise classification technique to detect benign and malignant tumors non-invasively from dermoscopic images. This paper proposes a novel lightweight deep learning model, Multiscale-FocusNet, for improved diagnosis of skin cancer. This work integrates advanced preprocessing and color consistency techniques with a novel dual attention model built on modified MobileNet-V2 architecture. The proposed method is validated on the public ISIC Challenge dataset. The proposed Multiscale-FocusNet exhibits exceptional performance, achieving 98.85% accuracy, 99.28% precision, 98.21% recall, and 98.74% F1-score. The Multiscale-FocusNet shows a superior promising performance as compared to existing transfer learning models. The proposed method can be effectively used for precise detection of skin cancer using dermoscopic images.

UR - https://www.scopus.com/pages/publications/105013063268

UR - https://www.scopus.com/pages/publications/105013063268#tab=citedBy

U2 - 10.1007/978-981-96-3355-5_30

DO - 10.1007/978-981-96-3355-5_30

M3 - Conference contribution

AN - SCOPUS:105013063268

SN - 9789819633548

T3 - Lecture Notes in Networks and Systems

SP - 377

EP - 389

BT - Proceedings of Data Analytics and Management, ICDAM 2024

A2 - Swaroop, Abhishek

A2 - Virdee, Bal

A2 - Correia, Sérgio Duarte

A2 - Polkowski, Zdzislaw

PB - Springer Science and Business Media Deutschland GmbH

Y2 - 14 June 2024 through 15 June 2024

ER -

Sinha S, Bhatt M, Areeckal AS. Multiscale-FocusNet: A Novel Deep Learning Approach for Skin Cancer Detection Using Dermoscopic Images. In Swaroop A, Virdee B, Correia SD, Polkowski Z, editors, Proceedings of Data Analytics and Management, ICDAM 2024. Springer Science and Business Media Deutschland GmbH. 2025. p. 377-389. (Lecture Notes in Networks and Systems). doi: 10.1007/978-981-96-3355-5_30

Multiscale-FocusNet: A Novel Deep Learning Approach for Skin Cancer Detection Using Dermoscopic Images

Abstract

Publication series

Conference

UN SDGs

All Science Journal Classification (ASJC) codes

Access to Document

Other files and links

Fingerprint

Cite this