Biomedical Informatics and Smart Healthcare
ISSN: 3068-5524 (Online)
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TY - JOUR AU - Negi, Simran AU - Joshi, Aditya PY - 2025 DA - 2025/12/31 TI - Transformer Enabled ResNet Based Automated Skin Cancer Detection System JO - Biomedical Informatics and Smart Healthcare T2 - Biomedical Informatics and Smart Healthcare JF - Biomedical Informatics and Smart Healthcare VL - 1 IS - 3 SP - 149 EP - 154 DO - 10.62762/BISH.2025.444143 UR - https://www.icck.org/article/abs/BISH.2025.444143 KW - ResNet50 KW - deep learning KW - convolutional neural networks KW - skin cancer AB - Early diagnosis plays a critical role in the successful skin cancer treatment. To classify skin lesions as benign or malignant, this study suggested a deep learning-enabled method that combines a Transformer module with ResNet50. At first ResNet50, a powerful convolutional neural network, to extract the image features and then enhance the model with a Transformer layer to improve the model accuracy is used. The final model is fine-tuned to achieve better accuracy. This approach shows improved classification results compared to the traditional model. The results signify that combining CNN-based feature extraction with Transformer-enabled global attention suggestively improves skin lesion classification, making the suggested framework a promising method for early and consistent skin cancer detection in intelligent healthcare systems. SN - 3068-5524 PB - Institute of Central Computation and Knowledge LA - English ER -
@article{Negi2025Transforme,
author = {Simran Negi and Aditya Joshi},
title = {Transformer Enabled ResNet Based Automated Skin Cancer Detection System},
journal = {Biomedical Informatics and Smart Healthcare},
year = {2025},
volume = {1},
number = {3},
pages = {149-154},
doi = {10.62762/BISH.2025.444143},
url = {https://www.icck.org/article/abs/BISH.2025.444143},
abstract = {Early diagnosis plays a critical role in the successful skin cancer treatment. To classify skin lesions as benign or malignant, this study suggested a deep learning-enabled method that combines a Transformer module with ResNet50. At first ResNet50, a powerful convolutional neural network, to extract the image features and then enhance the model with a Transformer layer to improve the model accuracy is used. The final model is fine-tuned to achieve better accuracy. This approach shows improved classification results compared to the traditional model. The results signify that combining CNN-based feature extraction with Transformer-enabled global attention suggestively improves skin lesion classification, making the suggested framework a promising method for early and consistent skin cancer detection in intelligent healthcare systems.},
keywords = {ResNet50, deep learning, convolutional neural networks, skin cancer},
issn = {3068-5524},
publisher = {Institute of Central Computation and Knowledge}
}
Copyright © 2025 by the Author(s). Published by Institute of Central Computation and Knowledge. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/), which permits 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 licence, and indicate if changes were made. 
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