The rapid increase in population density has posed significant challenges to medical sciences in the auto-detection of various diseases. Intelligent systems play a crucial role in assisting medical professionals with early disease detection and providing consistent treatment, ultimately reducing mortality rates. Skin-related diseases, particularly those that can become severe if not detected early, require timely identification to expedite diagnosis and improve patient outcomes. This paper proposes a transfer learning-based ensemble deep learning model for diagnosing dermatological conditions at an early stage. Data augmentation techniques were employed to increase the number of samples and create a diverse data pattern within the dataset. The study applied ResNet50, InceptionV3, and DenseNet121 transfer learning models, leading to the development of a weighted and average ensemble model. The system was trained and tested using the International Skin Imaging Collaboration (ISIC) dataset. The proposed ensemble model demonstrated superior performance, achieving 98.5% accuracy, 97.50% Kappa, 97.67% MCC (Matthews Correlation Coefficient), and 98.50% F1 score. The model outperformed existing state-of-the-art models in dermatological disease classification and provides valuable support to dermatologists and medical specialists in early disease detection. Compared to previous research, the proposed model offers high accuracy with lower computational complexity, addressing a significant challenge in the classification of skin-related diseases.
Keywords
Skin Cancer, Transfer Learning, Medical Image Processing, Ensemble Learning, Deep Learning, ISIC.
F. Bray et al., “Comparing cancer and cardiovascular disease trends in 20 middle- or high-income countries 2000–19: A pointer to national trajectories towards achieving Sustainable Development goal target 3.4,” Cancer Treatment Reviews, vol. 100, p. 102290, Nov. 2021, doi: 10.1016/j.ctrv.2021.102290.
N. A. AlSadhan, S. A. Alamri, M. M. Ben Ismail, and O. Bchir, “Skin Cancer Recognition Using Unified Deep Convolutional Neural Networks,” Cancers, vol. 16, no. 7, p. 1246, Mar. 2024, doi: 10.3390/cancers16071246.
Mohammad Atikur Rahman, Ehsan Bazgir, S. M. Saokat Hossain, and Md. Maniruzzaman, “Skin cancer classification using NASNet,” International Journal of Science and Research Archive, vol. 11, no. 1, pp. 775–785, Jan. 2024, doi: 10.30574/ijsra.2024.11.1.0106.
B. Sarker, N. Bin Sharif, M. Atikur Rahman, and A. H. M. Shahariar Parvez, “AI, IoMT and Blockchain in Healthcare,” Journal of Trends in Computer Science and Smart Technology, vol. 5, no. 1, pp. 30–50, Apr. 2023, doi: 10.36548/jtcsst.2023.1.003.
A. Naeem, T. Anees, M. Khalil, K. Zahra, R. A. Naqvi, and S.-W. Lee, “SNC_Net: Skin Cancer Detection by Integrating Handcrafted and Deep Learning-Based Features Using Dermoscopy Images,” Mathematics, vol. 12, no. 7, p. 1030, Mar. 2024, doi: 10.3390/math12071030.
K. M. Monica, J. Shreeharsha, P. Falkowski-Gilski, B. Falkowska-Gilska, M. Awasthy, and R. Phadke, “Melanoma skin cancer detection using mask-RCNN with modified GRU model,” Frontiers in Physiology, vol. 14, Jan. 2024, doi: 10.3389/fphys.2023.1324042.
M. T. Campos et al., “New MoS2/Tegafur-Containing Pharmaceutical Formulations for Selective LED-Based Skin Cancer Photo-Chemotherapy,” Pharmaceutics, vol. 16, no. 3, p. 360, Mar. 2024, doi: 10.3390/pharmaceutics16030360.
A. Naeem and T. Anees, “DVFNet: A deep feature fusion-based model for the multiclassification of skin cancer utilizing dermoscopy images,” PLOS ONE, vol. 19, no. 3, p. e0297667, Mar. 2024, doi: 10.1371/journal.pone.0297667.
B. S. Puttaswamy and N. Thillaiarasu, “Fine DenseNet based human personality recognition using english hand writing of non-native speakers,” Biomedical Signal Processing and Control, vol. 99, p. 106910, Jan. 2025, doi: 10.1016/j.bspc.2024.106910.
I. Kousis, I. Perikos, I. Hatzilygeroudis, and M. Virvou, “Deep Learning Methods for Accurate Skin Cancer Recognition and Mobile Application,” Electronics, vol. 11, no. 9, p. 1294, Apr. 2022, doi: 10.3390/electronics11091294.
A. V. P. Rajesh, K. N. Rao, G. N. V. Sai, K. D. Kumar, and K. R. S. Karthik, “Skin Cancer Detection and Intensity Analysis using Deep Learning,” 2024 International Conference on Emerging Systems and Intelligent Computing (ESIC), pp. 376–381, Feb. 2024, doi: 10.1109/esic60604.2024.10481663.
M. S. Sivakumar, L. M. Leo, T. Gurumekala, V. Sindhu, and A. S. Priyadharshini, “Deep learning in skin lesion analysis for malignant melanoma cancer identification,” Multimedia Tools and Applications, vol. 83, no. 6, pp. 17833–17853, Jul. 2023, doi: 10.1007/s11042-023-16273-1.
M. Ashwin Shenoy and N. Thillaiarasu, “Enhancing temple surveillance through human activity recognition: A novel dataset and YOLOv4-ConvLSTM approach,” Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11217–11232, Dec. 2023, doi: 10.3233/jifs-233919.
J. V. Tembhurne, N. Hebbar, H. Y. Patil, and T. Diwan, “Skin cancer detection using ensemble of machine learning and deep learning techniques,” Multimedia Tools and Applications, vol. 82, no. 18, pp. 27501–27524, Feb. 2023, doi: 10.1007/s11042-023-14697-3.
A. Shahsavari, T. Khatibi, and S. Ranjbari, “Skin lesion detection using an ensemble of deep models: SLDED,” Multimedia Tools and Applications, vol. 82, no. 7, pp. 10575–10594, Sep. 2022, doi: 10.1007/s11042-022-13666-6.
M. Shorfuzzaman, “An explainable stacked ensemble of deep learning models for improved melanoma skin cancer detection,” Multimedia Systems, vol. 28, no. 4, pp. 1309–1323, Apr. 2021, doi: 10.1007/s00530-021-00787-5.
K. Munuswamy Selvaraj, S. Gnanagurusubbiah, R. R. Roby Roy, J. H. John peter, and S. Balu, “Enhancing skin lesion classification with advanced deep learning ensemble models: a path towards accurate medical diagnostics,” Current Problems in Cancer, vol. 49, p. 101077, Apr. 2024, doi: 10.1016/j.currproblcancer.2024.101077.
K. Ali, Z. A. Shaikh, A. A. Khan, and A. A. Laghari, “Multiclass skin cancer classification using EfficientNets – a first step towards preventing skin cancer,” Neuroscience Informatics, vol. 2, no. 4, p. 100034, Dec. 2022, doi: 10.1016/j.neuri.2021.100034.
T. M. Alam et al., “An Efficient Deep Learning-Based Skin Cancer Classifier for an Imbalanced Dataset,” Diagnostics, vol. 12, no. 9, p. 2115, Aug. 2022, doi: 10.3390/diagnostics12092115.
A. Demir, F. Yilmaz, and O. Kose, “Early detection of skin cancer using deep learning architectures: resnet-101 and inception-v3,” 2019 Medical Technologies Congress (TIPTEKNO), Oct. 2019, doi: 10.1109/tiptekno47231.2019.8972045.
M. Ravi Prasad and N. Thillaiarasu, “Multichannel EfficientNet B7 with attention mechanism using multimodal biometric- based authentication for ATM transaction,” Multiagent and Grid Systems, vol. 20, no. 2, pp. 89–108, Aug. 2024, doi: 10.3233/mgs-230118.
K. M. Hosny, M. A. Kassem, and M. M. Foaud, “Skin Cancer Classification using Deep Learning and Transfer Learning,” 2018 9th Cairo International Biomedical Engineering Conference (CIBEC), Dec. 2018, doi: 10.1109/cibec.2018.8641762.
Acknowledgements
Author(s) thanks to Dr. Gopinath M P for this research completion and support.
Funding
No funding was received to assist with the preparation of this manuscript.
Ethics declarations
Conflict of interest
The authors have no conflicts of interest to declare that are relevant to the content of this article.
Availability of data and materials
Data sharing is not applicable to this article as no new data were created or analysed in this study.
Author information
Contributions
All authors have equal contribution in the paper and all authors have read and agreed to the published version of the manuscript.
Corresponding author
Gopinath M P
School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India.
Open Access This article is licensed under a Creative Commons Attribution NoDerivs is a more restrictive license. It allows you to redistribute the material commercially or non-commercially but the user cannot make any changes whatsoever to the original, i.e. no derivatives of the original work. To view a copy of this license, visit https://creativecommons.org/licenses/by-nc-nd/4.0/
Cite this article
Akepati Sankar Reddy and Gopinath M P, “A Computing Framework for Transfer Learning and Ensemble Classification of Surface Patterns”, Journal of Machine and Computing. doi: 10.53759/7669/jmc202505011.
