YOLOv11 with spatial attention and preprocessing enhancements for accurate skin cancer classification
DOI:
https://doi.org/10.29194/NJES.29010141Keywords:
Deep Learning , Medical Image Analysis , Melanoma Detection, Skin Cancer Classification, YOLOv11Abstract
This work suggests a Deep Learning (DL) architecture based on You Only Look Once YOLOv11 for Skin Cancer (SC) detection. The similarity between malignant and benign lesions makes visual inspection a failure to distinguish between them. To solve this problem, the proposed approach uses a 3-step pre-processing stage, namely hair removal, color normalization, and Contrast Limited Adaptive Histogram Equalization (CLAHE) contrasts, has been conducted to eliminate artifacts and improve image quality. Balanced data augmentation on the training set of the PROVe-AI dataset. In this process, YOLOv11 with C3k2 module and C2PSA module showed significant results in optimized multi-scale feature collection and spatial interest. The experimental outcome demonstrates that the proposed model has a classification accuracy of 93.09% and led the baseline models, such as Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Artificial Neural Network (ANN). The proposed optimized YOLOv11 architecture allows for skin cancer detection in a computationally efficient framework with promising preliminary results so that the proposed approach can be a beneficial Artificial intelligence (AI) tool for early diagnosis, particularly in a lack of high-tech medical facilities.
Downloads
References
B. Ozdemir and I. Pacal, "An innovative deep learning framework for skin cancer detection employing ConvNeXtV2 and focal self‑attention mechanisms," Results Eng., vol. 25, p. 103692, 2025.
https://doi.org/10.1016/j.rineng.2024.103692 DOI: https://doi.org/10.1016/j.rineng.2024.103692
H. J. Mohammed, A. A. Nafea, H. K. Almulla, S. A. S. Aliesawi, and M. M. Al‑Ani, "An effective hybrid model for skin cancer detection using transfer learning," in Proc. 2023 16th Int. Conf. Developments eSystems Eng. (DeSE), IEEE, 2023, pp. 840-845.
https://doi.org/10.1109/DeSE60595.2023.10468994 DOI: https://doi.org/10.1109/DeSE60595.2023.10468994
R. Karthik, R. Menaka, S. Atre, J. Cho, and S. V. Easwaramoorthy, "A hybrid deep learning approach for skin cancer classification using Swin Transformer and dense group shuffle non‑local attention network," IEEE Access, 2024.
https://doi.org/10.1109/ACCESS.2024.3485507 DOI: https://doi.org/10.1109/ACCESS.2024.3485507
A. Kumar, A. Vishwakarma, V. Bajaj, and S. Mishra, "Novel mixed domain hand‑crafted features for skin disease recognition using multiheaded CNN," IEEE Trans. Instrum. Meas., vol. 73, pp. 1-13, 2024.
https://doi.org/10.1109/TIM.2024.3370772 DOI: https://doi.org/10.1109/TIM.2024.3370772
H. L. Gururaj, N. Manju, A. Nagarjun, V. N. M. Aradhya, and F. Flammini, "DeepSkin: a deep learning approach for skin cancer classification," IEEE Access, vol. 11, pp. 50205-50214, 2023.
https://doi.org/10.1109/ACCESS.2023.3274848 DOI: https://doi.org/10.1109/ACCESS.2023.3274848
V. S. S. B. T. Sathvika et al., "Pipelined structure in the classification of skin lesions based on AlexNet CNN and SVM model with bi‑sectional texture features," IEEE Access, vol. 12, pp. 57366-57380, 2024.
https://doi.org/10.1109/ACCESS.2024.3387533 DOI: https://doi.org/10.1109/ACCESS.2024.3387533
I. F. Jassam, A. A. Mukhlif, A. A. Nafea, M. A. Tharthar, and A. I. Khudhair, "A review of breast cancer histological image classification: challenges and limitations," Iraqi J. Comput. Sci. Math., vol. 6, no. 1, p. 1, 2025.
https://doi.org/10.52866/2788-7421.1232 DOI: https://doi.org/10.52866/2788-7421.1232
A. A. Nafea, A.‑M. Manar, K. M. A. Alheeti, M. S. I. Alsumaidaie, and M. M. Al‑Ani, "A hybrid method of 1D‑CNN and machine learning algorithms for breast cancer detection," Baghdad Sci. J., vol. 21, no. 10, pp. 3333-3343, 2024.
https://doi.org/10.21123/bsj.2024.9443 DOI: https://doi.org/10.21123/bsj.2024.9443
Q. Yao, D. Zhuang, Y. Feng, Y. Wang, and J. Liu, "Accurate detection of brain tumor lesions from medical images based on improved YOLOv8 algorithm," IEEE Access, 2024.
https://doi.org/10.1109/ACCESS.2024.3472039 DOI: https://doi.org/10.1109/ACCESS.2024.3472039
Y. Lin, C. Dong, H. Long, J. Liang, M. Liu, and Y. Liu, "Algorithm research for melanoma detection based on improved YOLOv11," in Proc. 2025 10th Int. Conf. Intell. Comput. Signal Process. (ICSP), IEEE, 2025, pp. 557-560.
https://doi.org/10.1109/ICSP65755.2025.11086968 DOI: https://doi.org/10.1109/ICSP65755.2025.11086968
A. T. Ibrahim, M. Abdullahi, A. F. D. Kana, M. T. Mohammed, and I. H. Hassan, "Categorical classification of skin cancer using a weighted ensemble of transfer learning with test time augmentation," Data Sci. Manag., vol. 8, no. 2, pp. 174-184, 2025.
https://doi.org/10.1016/j.dsm.2024.10.002 DOI: https://doi.org/10.1016/j.dsm.2024.10.002
A. Pandey, M. S. Teja, P. Sahare, V. Kamble, M. Parate, and M. F. Hashmi, "Skin cancer classification using non‑local means denoising and sparse dictionary learning based CNN," J. Electr. Syst. Inf. Technol., vol. 11, no. 1, p. 36, 2024.
https://doi.org/10.1186/s43067-024-00162-0 DOI: https://doi.org/10.1186/s43067-024-00162-0
R. A. Diptho and S. Basak, "Enhancing dermatological diagnosis through medical image analysis: how effective is YOLO11 compared to leading CNN models?," NDT, vol. 3, no. 2, p. 11, 2025.
https://doi.org/10.3390/ndt3020011 DOI: https://doi.org/10.3390/ndt3020011
N.‑C. Huang, A. Mukundan, R. Karmakar, S. Syna, W.‑Y. Chang, and H.‑C. Wang, "Novel snapshot‑based hyperspectral conversion for dermatological lesion detection via YOLO object detection models," Bioengineering, vol. 12, no. 7, p. 714, 2025.
https://doi.org/10.3390/bioengineering12070714 DOI: https://doi.org/10.3390/bioengineering12070714
H. Erbay, Y. M. Abulgasim, D. E. Özer, and F. Ertürk, "Enhancing multi‑class skin lesion diagnosis through ensemble learning of CNN and transformer architectures," Eng. Sci. Technol. Int. J., vol. 70, p. 102145, 2025.
https://doi.org/10.1016/j.jestch.2025.102145 DOI: https://doi.org/10.1016/j.jestch.2025.102145
A. Shaik, S. S. Dutta, I. M. Sawant, S. Kumar, A. Balasundaram, and K. De, "An attention‑based hybrid approach using CNN and BiLSTM for improved skin lesion classification," Sci. Rep., vol. 15, no. 1, p. 15680, 2025.
https://doi.org/10.1038/s41598-025-00025-2 DOI: https://doi.org/10.1038/s41598-025-00025-2
P. Nanda, D. Rout, and S. Kumari, "Multi‑class skin cancer detection using CNN‑architecture based deep learning models," Procedia Comput. Sci., vol. 260, pp. 226-235, 2025.
https://doi.org/10.1016/j.procs.2025.03.197 DOI: https://doi.org/10.1016/j.procs.2025.03.197
A. Asriani, N. T. Lapatta, D. W. Nugraha, A. Amriana, and W. Wirdayanti, "Implementation of ResNet‑50‑based convolutional neural network for mobile skin cancer classification," J. Appl. Informatics Comput., vol. 9, no. 4, pp. 1577-1969, 2025.
https://doi.org/10.30871/jaic.v9i4.9696 DOI: https://doi.org/10.30871/jaic.v9i4.9696
S. Mondal, R. Bonthagorla, H. Kotapati, H. Tummala, and D. Khatua, "Deep learning for skin cancer: hybrid feature extraction with GoogleNet and MobileNet," in Proc. 2025 Int. Conf. Adv. Modern Age Technol. Health Eng. Sci. (AMATHE), IEEE, 2025, pp. 1-6.
https://doi.org/10.1109/AMATHE65477.2025.11080891 DOI: https://doi.org/10.1109/AMATHE65477.2025.11080891
U. Ilyas, M. Fuzail, M. K. Abid, T. F. Khan, A. Naeem, and N. Aslam, "Multiclass skin cancer classification using ResNet and dermoscopic imaging," Spectr. Eng. Sci., vol. 3, no. 6, pp. 671-685, 2025.
M. A. Marchetti et al., "Prospective validation of dermoscopy‑based open‑source artificial intelligence for melanoma diagnosis (PROVE‑AI study)," NPJ Digit. Med., vol. 6, no. 1, p. 127, 2023.
https://doi.org/10.1038/s41746-023-00872-1 DOI: https://doi.org/10.1038/s41746-023-00872-1
R. Kasmi et al., "SharpRazor: automatic removal of hair and ruler marks from dermoscopy images," Skin Res. Technol., vol. 29, no. 4, p. e13203, 2023.
https://doi.org/10.1111/srt.13203 DOI: https://doi.org/10.1111/srt.13203
X. Shen, L. Wei, and S. Tang, "Dermoscopic image classification method using an ensemble of fine‑tuned convolutional neural networks," Sensors, vol. 22, no. 11, p. 4147, 2022.
https://doi.org/10.3390/s22114147 DOI: https://doi.org/10.3390/s22114147
S. Barın and G. E. Güraksın, "An automatic skin lesion segmentation system with hybrid FCN‑ResAlexNet," Eng. Sci. Technol. Int. J., vol. 34, p. 101174, 2022.
https://doi.org/10.1016/j.jestch.2022.101174 DOI: https://doi.org/10.1016/j.jestch.2022.101174
S. Joseph and O. O. Olugbara, "Preprocessing effects on performance of skin lesion saliency segmentation," Diagnostics, vol. 12, no. 2, p. 344, 2022.
https://doi.org/10.3390/diagnostics12020344 DOI: https://doi.org/10.3390/diagnostics12020344
F. Perez, C. Vasconcelos, S. Avila, and E. Valle, "Data augmentation for skin lesion analysis," in Proc. Int. Workshop Comput.‑Assisted Robot. Endoscopy, Springer, 2018, pp. 303-311.
https://doi.org/10.1007/978-3-030-01201-4_33 DOI: https://doi.org/10.1007/978-3-030-01201-4_33
M. Alsaidi, M. T. Jan, A. Altaher, H. Zhuang, and X. Zhu, "Tackling the class imbalanced dermoscopic image classification using data augmentation and GAN," Multimed. Tools Appl., vol. 83, no. 16, pp. 49121-49147, 2024.
https://doi.org/10.1007/s11042-023-17067-1 DOI: https://doi.org/10.1007/s11042-023-17067-1
A. Shah et al., "A comprehensive study on skin cancer detection using artificial neural network (ANN) and convolutional neural network (CNN)," Clin. eHealth, vol. 6, pp. 76-84, 2023.
https://doi.org/10.1016/j.ceh.2023.08.002 DOI: https://doi.org/10.1016/j.ceh.2023.08.002
A. Ashtikar and S. Shastri, "A CNN model for skin cancer detection and classification by using image processing techniques," J. Sci. Res. Technol., pp. 251-263, 2025.
S. S. Zareen, G. Sun, M. Kundi, S. F. Qadri, and S. Qadri, "Enhancing skin cancer diagnosis with deep learning: a hybrid CNN‑RNN approach," Comput. Mater. Contin., vol. 79, no. 1, 2024.
https://doi.org/10.32604/cmc.2024.047418 DOI: https://doi.org/10.32604/cmc.2024.047418
I. R. Putri, A. Amalia, and T. F. Abidin, "Hybrid algorithm using CNN and LSTM to improve performance in skin lesion image classification," in Proc. 2024 Ninth Int. Conf. Informatics Comput. (ICIC), IEEE, 2024, pp. 1-6.
https://doi.org/10.1109/ICIC64337.2024.10956620 DOI: https://doi.org/10.1109/ICIC64337.2024.10956620
H. K. Verma, N. D. Londhe, A. Kumar, and R. S. Sonawane, "YOLOv11 for psoriasis classification: enhancing feature extraction for accurate severity differentiation," in Proc. 2025 Int. Conf. Electron., AI Comput. (EAIC), IEEE, 2025, pp. 1-6.
https://doi.org/10.1109/EAIC66483.2025.11101292 DOI: https://doi.org/10.1109/EAIC66483.2025.11101292
R. Khanam and M. Hussain, "YOLOv11: an overview of the key architectural enhancements," arXiv Prepr. arXiv2410.17725, 2024.
Q. Zhao and J. Zhu, "An improved YOLOv11 architecture with multi‑scale attention and spatial fusion for fine‑grained residual detection," Results Eng., p. 107061, 2025.
https://doi.org/10.1016/j.rineng.2025.107061 DOI: https://doi.org/10.1016/j.rineng.2025.107061
A. K. Kareem and K. M. A. Alheeti, "Hybrid approach for fall detection based on machine learning," in Proc. Int. Conf. New Trends Inf. Commun. Technol. Appl., Springer, 2021, pp. 111-130.
https://doi.org/10.1007/978-3-030-93417-0_8 DOI: https://doi.org/10.1007/978-3-030-93417-0_8
K. M. A. Alheeti, A. Alzahrani, M. Alamri, A. K. Kareem, and D. Al_Dosary, "A comparative study for SDN security based on machine learning," Int. J. Interact. Mob. Technol., vol. 17, no. 11, 2023.
https://doi.org/10.3991/ijim.v17i11.39065 DOI: https://doi.org/10.3991/ijim.v17i11.39065
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Aythem Khairi Kareem, Ahmed Adil Nafea, Afrig Aminuddin
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
The authors retain the copyright of their manuscript by submitting the work to this journal, and all open access articles are distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC-BY-NC 4.0), which permits use for any non-commercial purpose, distribution, and reproduction in any medium, provided that the original work is properly cited.
How to Cite
