Skin Lesions Classification Using Deep Ensemble Learning Model of VGG-16, ResNet-50 and Inception-V3

References

[1] A. Murugan, S. A. H. Nair, A. A. P. Preethi, and K. P. S. Kumar, “Diagnosis of skin cancer using machine learning techniques,” Microprocess. Microsyst., vol. 81, p. 103727, Mar. 2021, doi: 10.1016/j.micpro.2020.103727.

[2] G. Akilandasowmya, G. Nirmaladevi, S. Suganthi, and A. Aishwariya, “Skin cancer diagnosis: Leveraging deep hidden features and ensemble classifiers for early detection and classification,” Biomed. Signal Process. Control, vol. 88, p. 105306, Feb. 2024, doi: 10.1016/j.bspc.2023.105306.

[3] L. Talavera-Martínez, P. Bibiloni, A. Giacaman, R. Taberner, L. J. D. P. Hernando, and M. González-Hidalgo, “A novel approach for skin lesion symmetry classification with a deep learning model,” Comput. Biol. Med., vol. 145, p. 105450, Jun. 2022, doi: 10.1016/j.compbiomed.2022.105450.

[4] A. Bibi et al., “Skin Lesion Segmentation and Classification Using Conventional and Deep Learning Based Framework,” Comput. Mater. Contin., vol. 71, no. 2, pp. 2477–2495, 2022, doi: 10.32604/cmc.2022.018917.

[5] W. Gouda, N. U. Sama, G. Al-Waakid, M. Humayun, and N. Z. Jhanjhi, “Detection of Skin Cancer Based on Skin Lesion Images Using Deep Learning,” Healthcare, vol. 10, no. 7, p. 1183, Jun. 2022, doi: 10.3390/healthcare10071183.

[6] A. K. Adepu, S. Sahayam, U. Jayaraman, and R. Arramraju, “Melanoma classification from dermatoscopy images using knowledge distillation for highly imbalanced data,” Comput. Biol. Med., vol. 154, p. 106571, Mar. 2023, doi: 10.1016/j.compbiomed.2023.106571.

[7] A. Alhudhaif, B. Almaslukh, A. O. Aseeri, O. Guler, and K. Polat, “A novel nonlinear automated multi-class skin lesion detection system using soft-attention based convolutional neural networks,” Chaos, Solitons & Fractals, vol. 170, p. 113409, May 2023, doi: 10.1016/j.chaos.2023.113409.

[8] N. Priyadharshini, S. N., B. Hemalatha, and C. Sureshkumar, “A novel hybrid Extreme Learning Machine and Teaching–Learning- Based Optimization algorithm for skin cancer detection,” Healthc. Anal., vol. 3, p. 100161, Nov. 2023, doi: 10.1016/j.health.2023.100161.

[9] H. K. Gajera, D. R. Nayak, and M. A. Zaveri, “A comprehensive analysis of dermoscopy images for melanoma detection via deep CNN features,” Biomed. Signal Process. Control, vol. 79, p. 104186, Jan. 2023, doi: 10.1016/j.bspc.2022.104186.

[10] S. K. Singh, S. Banerjee, A. Chakraborty, and A. Bandyopadhyay, “Classification of Melanoma Skin Cancer Using Inception-ResNet,” in Frontiers of ICT in Healthcare, 2023, pp. 65–74. doi: 10.1007/978-981-19-5191-6_6.

[11] Y. Wang et al., “Adversarial multimodal fusion with attention mechanism for skin lesion classification using clinical and dermoscopic images,” Med. Image Anal., vol. 81, p. 102535, Oct. 2022, doi: 10.1016/j.media.2022.102535.

[12] D. Zhuang, K. Chen, and J. M. Chang, “CS-AF: A cost-sensitive multi-classifier active fusion framework for skin lesion classification,” Neurocomputing, vol. 491, pp. 206–216, Jun. 2022, doi: 10.1016/j.neucom.2022.03.042.

[13] U. A. Lyakhova and P. A. Lyakhov, “Systematic review of approaches to detection and classification of skin cancer using artificial intelligence: Development and prospects,” Comput. Biol. Med., vol. 178, p. 108742, Aug. 2024, doi: 10.1016/j.compbiomed.2024.108742.

[14] M. Hajiarbabi and R. Chandra, “Skin Lesion Detection Using Deep Learning,” J. Autom. Mob. Robot. Intell. Syst., pp. 56–64, Aug. 2023, doi: 10.14313/JAMRIS/3-2022/24.

[15] M. A. Araaf, K. Nugroho, and D. R. I. M. Setiadi, “Comprehensive Analysis and Classification of Skin Diseases based on Image Texture Features using K-Nearest Neighbors Algorithm,” J. Comput. Theor. Appl., vol. 1, no. 1, pp. 31–40, Sep. 2023, doi: 10.33633/jcta.v1i1.9185.

[16] M. T. H. Khan Tusar, M. T. Islam, A. H. Sakil, M. N. H. N. Khandaker, and M. M. Hossain, “An Intelligent Telediagnosis of Acute Lymphoblastic Leukemia using Histopathological Deep Learning,” J. Comput. Theor. Appl., vol. 2, no. 1, pp. 1–12, May 2024, doi: 10.62411/jcta.10358.

[17] J. Han, M. Kamber, and J. Pei, Data Mining: Concepts and Techniques. Elsevier Inc., 2012. doi: 10.1016/C2009-0-61819-5.

[18] J. Brownlee, “A Gentle Introduction to Transfer Learning for Deep Learning,” Machine Learning Mastery, 2019. https://machinelearningmastery.com/transfer-learning-for-deep-learning/

[19] M. ul Hassan, “VGG16 – Convolutional Network for Classification and Detection,” Neurohive.io, 2018. https://neurohive.io/en/popular-networks/vgg16/

[20] K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” in arXiv, Dec. 2015, vol. 2016-Decem, pp. 770–778. doi: 10.1109/CVPR.2016.90.

[21] S. Mukherjee, “The Annotated ResNet-50,” Towards Data Science, 2022. https://towardsdatascience.com/the-annotated-resnet-50- a6c536034758

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@article{1708324,
      author = {Shubham Chaurasia, Shiwangi Choudhary},
      title = {Skin Lesions Classification Using Deep Ensemble Learning Model of VGG-16, ResNet-50 and Inception-V3},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {8},
      number = {11},
      pages = {284-295},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1708324.pdf},
      abstract = {This research introduces a sophisticated methodology for the classification of multi-class skin lesions by utilizing an ensemble model that integrates the Inception-V3, ResNet-50, and VGG16 architectures. The primary objective of the classification task is to categorize skin lesions into specific classes, such as Melanoma (MEL), basal cell carcinoma (BCC), and Nevus (NEV), employing the ISIC dataset, which is a comprehensive repository of dermoscopic images. To address the issue of dataset imbalance, an oversampling strategy is implemented, as certain lesion types are underrepresented. This approach ensures that the training of model is done on a more balanced dataset, thereby enhancing its ability to classify all lesion types accurately and fairly. The ensemble technique capitalizes on the unique strengths of ResNet-50, Inception-V3, and VGG16, leading to improved overall classification performance. ResNet-50 is selected for its proficiency in deep feature extraction, which is essential for capturing intricate details in lesion patterns. Inception-V3 is chosen for its capability to process lesions at multiple scales, effectively analyzing variations in resolution and size. VGG16 is incorporated due to its straightforward yet highly efficient architecture for image classification tasks. The ensemble model, augmented with data enhancement techniques, significantly surpasses the performance of individual models in skin lesion classification, achieving superior results in precision, accuracy, recall, and F1-score for both the original and balanced ISIC datasets. This approach provides a robust framework for skin lesion classification, thereby enhancing the reliability and accuracy of diagnostic process in the field of dermatology.},
      keywords = {Skin Lesion Classification, Ensemble Model, ResNet-50, VGG-16, Inception-V3},
      month = {May},
  }