Skin Lesion Classification towards Melanoma Detection Using EfficientNetB3

  • Saumya Salian Department of Computer Engineering, Datta Meghe College of Engineering, Mumbai University, Mumbai, India
  • Sudhir Sawarkar Department of Computer Engineering, Datta Meghe College of Engineering, Mumbai University, Mumbai, India
Keywords: malignant, skin lesion, deep learning, classification

Abstract

The rise of incidences of melanoma skin cancer is a global health problem. Skin cancer, if diagnosed at an early stage, enhances the chances of a patient’s survival. Building an automated and effective melanoma classification system is the need of the hour. In this paper, an automated computer-based diagnostic system for melanoma skin lesion classification is presented using fine-tuned EfficientNetB3 model over ISIC 2017 dataset. To improve classification results, an automated image pre-processing phase is incorporated in this study, it can effectively remove noise artifacts such as hair structures and ink markers from dermoscopic images. Comparative analyses of various advanced models like ResNet50, InceptionV3, InceptionResNetV2, and EfficientNetB0-B2 are conducted to corroborate the performance of the proposed model. The proposed system also addressed the issue of model overfitting and achieved a precision of 88.00%, an accuracy of 88.13%, recall of 88%, and F1-score of 88%.

References

“Cancer Facts and Figures 2021,” https://www.cancer.org/content/dam/cancer-org/research/cancer-facts-and-statistics/annual-cancer-facts-and-figures/2021/cancer-facts-and-figures-2021.html, December 01, 2021.

S. Sonthalia, S. Yumeen, and F. Kaliyadan, “Dermoscopy Overview and Extradiagnostic Applications,” https://www.ncbi.nlm.nih.gov/books/NBK537131/, August 13, 2021.

K. Munir, H. Elahi, A. Ayub, F. Frezza, and A. Rizzi, “Cancer Diagnosis Using Deep Learning: A Bibliographic Review,” Cancers (Basel), vol. 11, no. 9, article no. 1235, August 2019.

“ISIC Challenge Datasets,” https://challenge.isic-archive.com/data/, December 01, 2021.

P. Naronglerdrit, I. Mporas, M. Paraskevas, and V. Kapoulas, “Melanoma Detection from Dermatoscopic Images Using Deep Convolutional Neural Networks,” International Conference on Biomedical Innovations and Applications (BIA), pp. 13-16, November 2020.

N. Siddique, S. Paheding, Md. Z. Alom, and V. Devabhaktuni, “Recurrent Residual U-Net with EfficientNet Encoder for Medical Image Segmentation,” Pattern Recognition and Tracking XXXII, pp. 1-10, April 2021.

S. S. Chaturvedi, K. Gupta, and P. S. Prasad, “Skin Lesion Analyser: An Efficient Seven-Way Multi-Class Skin Cancer Classification Using MobileNet,” International Conference on Advanced Machine Learning Technologies and Applications, pp. 165-176, February 2020.

R. Zhang, “Melanoma Detection Using Convolutional Neural Network,” IEEE International Conference on Consumer Electronics and Computer Engineering (ICCECE), pp. 75-78, January 2021.

Y. Zhang and C. Wang, “SIIM-ISIC Melanoma Classification with DenseNet,” IEEE 2nd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE), pp. 14-17, March 2021.

L. K. Ashim, N. Suresh, and C. V. Prasannakumar, “A Comparative Analysis of Various Transfer Learning Approaches Skin Cancer Detection,” 5th International Conference on Trends in Electronics and Informatics (ICOEI), pp. 1379-1385, June 2021.

Y. Chen, Y. Zhu, and Y. Chang, “CycleGAN Based Data Augmentation for Melanoma Images Classification,” Proceedings of the 3rd International Conference on Artificial Intelligence and Pattern Recognition, pp. 115-119, June 2020.

D. N. T. Le, H. X. Le, L. T. Ngo, and H. T. Ngo, “Transfer Learning with Class-Weighted and Focal Loss Function for Automatic Skin Cancer Classification,” https://arxiv.org/abs/2009.05977, September 13, 2020.

M. Manzo and S. Pellino, “Bucket of Deep Transfer Learning Features and Classification Models for Melanoma Detection,” Journal of Imaging, vol. 6, no. 12, pp. 1-15, November 2020.

M. A. Kadampur and S. A. Riyaee, “Skin Cancer Detection: Applying a Deep Learning Based Model Driven Architecture in the Cloud for Classifying Dermal Cell Images,” Informatics in Medicine Unlocked, vol. 18, no. 100282, pp. 1-6, 2020.

M. F. J. Acosta, L. Y. C. Tovar, M. B. G. Zapirain, and W. S. Percybrooks, “Melanoma Diagnosis Using Deep Learning Techniques on Dermatoscopic Images,” BMC Medical Imaging, vol. 21, no. 1, pp. 1-11, January 2021.

N. Rezaoana, M. S. Hossain, and K. Andersson, “Detection and Classification of Skin Cancer by Using a Parallel CNN Model,” IEEE International Women in Engineering (WIE) Conference on Electrical and Computer Engineering (WIECON-ECE), pp. 380-386, December 2020.

H. Talebi and P. Milanfar, “Learning to Resize Images for Computer Vision Tasks,” IEEE/CVF International Conference on Computer Vision (ICCV), pp. 497-506, October 2021.

M. K. Tekleyohannes, C. Weis, N. Wehn, M. Klein, and M. Siegrist, “A Reconfigurable Accelerator for Morphological Operations,” IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), pp. 186-193, May 2018.

S. Chatterjee, D. Dey, and S. Munshi, “Integration of Morphological Preprocessing and Fractal Based Feature Extraction with Recursive Feature Elimination for Skin Lesion Types Classification,” Computer Methods Programs in Biomedicine, vol. 178, pp. 201-218, September 2019.

J. K. Winkler, C. Fink, F. Toberer, A. Enk, T. Deinlein, R. Hofmann-Wellenhof, et al., “Association between Surgical Skin Markings in Dermoscopic Images and Diagnostic Performance of a Deep Learning Convolutional Neural Network for Melanoma Recognition,” JAMA Dermatology, vol. 155, no. 10, pp. 1135-1141, October 2019.

A. Mikołajczyk and M. Grochowski, “Data Augmentation for Improving Deep Learning in Image Classification Problem,” IEEE International Interdisciplinary PhD Workshop (IIPhDW), pp. 117-122, May 2018.

M. Tan and Q. Le, “Efficientnet: Rethinking Model Scaling for Convolutional Neural Networks,” International Conference on Machine Learning, pp. 6105-6114, June 2019.

M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L. C. Chen, “MobileNetV2: Inverted Residuals and Linear Bottlenecks,” IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4510-4520, June 2018.

M. Lin, Q. Chen, and S. Yan, “Network in Network,” https://arxiv.org/abs/1312.4400, December 16, 2013.

S. R. Salian and S. D. Sawarkar, “Melanoma Skin Lesion Classification Using Improved EfficientnetB3,” Jordanian Journal of Computers and Information Technology (JJCIT), vol. 8, no. 1, pp. 45-56, March 2022.

C. Szegedy, S. Ioffe, V. Vanhoucke, and A. A. Alemi, “Inception-V4, Inception-ResNet and the Impact of Residual Connections on Learning,” 31st AAAI Conference on Artificial Intelligence (AAAI'17), pp. 4278-4284, February 2017.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770-778, June 2016.

C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the Inception Architecture for Computer Vision,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818-2826, June 2016.

Published
2022-09-22
How to Cite
[1]
Saumya Salian and Sudhir Sawarkar, “Skin Lesion Classification towards Melanoma Detection Using EfficientNetB3”, Adv. technol. innov., Sep. 2022.
Section
Articles