Proceedings of the International Conference on Advances and Applications in Artificial Intelligence (ICAAAI 2025)

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Skin Cancer Detection Using Deep Learning with EfficientNetB0

Authors
Apurv Verma1, *, Keshika Jangde1, Aryan Verma1, Piyush Dewangan1, Suryansh Sharma1
1Shri Shankaracharya Institute of Professional Management and Technology, Raipur, India
*Corresponding author.
Corresponding Author
Apurv Verma
Available Online 22 June 2025.
DOI
10.2991/978-94-6463-738-0_84How to use a DOI?
Keywords
Image-based Diagnosis; Skin Cancer Detection; EfficientNetB0; Deep Learning; Convolutional Neural Network (CNNs); Medical Image Analysis; Melanoma Classification
Abstract

In the world, Skin cancer is the predominant cause of mortality from cancer-related conditions. However, the manual diagnosis of skin cancer continues to present challenges, despite the fact that timely detection significantly enhances the likelihood of effective treatment. The objective of this study is to examine the use of deep learning, namely EfficientNetB0, for the objective of identification and categorizing skin cancer through the identification of dermatoscopic images. We make use of a pre-trained EfficientNetB0 model, develop it using a bespoke architecture, and evaluate its effectiveness with the help of the HAM10000 dataset, which contains more than 10,000 dermatoscopic images. The model's performance is evaluated using many measures, including accuracy, precision, recall, and F1 score, and we describe our findings in comparison to the approaches that are currently in use. The fact that the the model attains an accuracy rate of 64%, as demonstrated by our findings, has the potential to serve as an automated diagnostic instrument for the purpose of diagnosing skin cancer.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Advances and Applications in Artificial Intelligence (ICAAAI 2025)
Series
Advances in Intelligent Systems Research
Publication Date
22 June 2025
ISBN
978-94-6463-738-0
ISSN
1951-6851
DOI
10.2991/978-94-6463-738-0_84How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Apurv Verma
AU  - Keshika Jangde
AU  - Aryan Verma
AU  - Piyush Dewangan
AU  - Suryansh Sharma
PY  - 2025
DA  - 2025/06/22
TI  - Skin Cancer Detection Using Deep Learning with EfficientNetB0
BT  - Proceedings of the International Conference on Advances and Applications in Artificial Intelligence (ICAAAI 2025)
PB  - Atlantis Press
SP  - 1092
EP  - 1105
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-738-0_84
DO  - 10.2991/978-94-6463-738-0_84
ID  - Verma2025
ER  -