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Skin Cancer Classification Using Transfer Learning by VGG16 Architecture (Case Study on Kaggle Dataset)

DOI: 10.4236/jilsa.2023.153005, PP. 67-75

Keywords: Skin Cancer, Classification, VGG16, Transfer Learning, Deep Learning

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Abstract:

Skin cancer is a serious and potentially life-threatening disease that affects millions of people worldwide. Early detection and accurate diagnosis are critical for successful treatment and improved patient outcomes. In recent years, deep learning has emerged as a powerful tool for medical image analysis, including the diagnosis of skin cancer. The importance of using deep learning in diagnosing skin cancer lies in its ability to analyze large amounts of data quickly and accurately. This can help doctors make more informed decisions about patient care and improve overall outcomes. Additionally, deep learning models can be trained to recognize subtle patterns and features that may not be visible to the human eye, leading to earlier detection and more effective treatment. The pre-trained Visual Geometry Group 16 (VGG16) architecture has been used in this study to classification of skin cancer images, and the images have been converted into other color scales, there are named: 1) Hue Saturation Value (HSV), 2) YCbCr, 3) Grayscale for evaluation. Results show that the dataset created with RGB and YCbCr images in field condition was promising with a classification accuracy of 84.242%. The dataset has also been evaluated with other popular architectures and compared. The performance of VGG16 with images of each color scale is analyzed. In addition, feature parameters have been extracted from the different layers. The extracted layers were felt with the VGG16 to evaluate the ability of the feature parameters in classifying the disease.

References

[1]  Ali, M.S., Miah, M.S., Haque, J., Rahman, M.M. and Islam, M.K. (2021) An Enhanced Technique of Skin Cancer Classification Using Deep Convolutional Neural Network with Transfer Learning Models. Machine Learning with Applications, 5, Article ID: 100036.
https://doi.org/10.1016/j.mlwa.2021.100036
[2]  Carcagnì, P., Leo, M., Cuna, A., Mazzeo, P.L., Spagnolo, P., Celeste, G., et al. (2019) Classification of Skin Lesions by Combining Multilevel Learnings in a DenseNet Architecture. In: Ricci, E., Rota Bulò, S., Snoek, C., Lanz, O., Messelodi, S. and Sebe, N., Eds., Image Analysis and Processing—ICIAP 2019, Springer, Cham, 335-344.
https://doi.org/10.1007/978-3-030-30642-7_30
[3]  Dorj, U.O., Lee, K.K., Choi, J.Y. and Lee, M. (2018) The Skin Cancer Classification Using Deep Convolutional Neural Network. Multimedia Tools and Applications, 77, 9909-9924.
https://doi.org/10.1007/s11042-018-5714-1
[4]  Pacheco, A.G. and Krohling, R.A. (2019) Recent Advances in Deep Learning Applied to Skin Cancer Detection. arXiv: 1912.03280.
[5]  Ashraf, R., Afzal, S., Rehman, A.U., Gul, S., Baber, J., Bakhtyar, M., Mehmood, I., Song, O.Y. and Maqsood, M. (2020) Region-of-Interest Based Transfer Learning Assisted Framework for Skin Cancer Detection. IEEE Access, 8, 147858-147871.
https://doi.org/10.1109/ACCESS.2020.3014701
[6]  Byrd, A.L., Belkaid, Y. and Segre, J.A. (2018) The Human Skin Microbiome. Nature Reviews Microbiology, 16, 143-155.
https://doi.org/10.1038/nrmicro.2017.157
[7]  Jeong, M.K., Lu, J.C., Huo, X., Vidakovic, B. and Chen, D. (2006) Wavelet-Based Data Reduction Techniques for Process Fault Detection. Technometrics, 48, 26-40.
https://doi.org/10.1198/004017005000000553
[8]  Namey, E., Guest, G., Thairu, L. and Johnson, L. (2008) Data Reduction Techniques for Large Qualitative Data Sets. Handbook for Team-Based Qualitative Research, 2, 137-161.
[9]  Mahbod, A., Schaefer, G., Wang, C., Ecker, R. and Ellinge, I. (2019) Skin Lesion Classification Using Hybrid Deep Neural Networks. ICASSP 2019—2019 IEEE International Conference on Acoustics, Speech and Signal Processing, Brighton, 12-17 May 2019, 1229-1233.
https://doi.org/10.1109/ICASSP.2019.8683352
[10]  Sagar, A. and Dheeba, J. (2020) Convolutional Neural Networks for Classifying Melanoma Images. bioRxiv.
https://www.biorxiv.org/content/10.1101/2020.05.22.110973v2
[11]  Shaikh, J., Khan, R., Ingle, Y. and Shaikh, N. (2022) Skin Cancer Detection: A Review Using AI Techniques. International Journal of Health Sciences, 6, 14339-14346.
[12]  Althubiti, S.A., Alenezi, F., Shitharth, S., Sangeetha, K. and Simha Reddy, C.V. (2022) Circuit Manufacturing Defect Detection Using VGG16 Convolutional Neural Networks. Wireless Communications and Mobile Computing, 2022, Article ID: 1070405.
https://doi.org/10.1155/2022/1070405
[13]  Kalouche, S. (2016) Vision-Based Classification of Skin Cancer Using Deep Learning.
https://www.semanticscholar.org/paper/Vision-Based-Classification-of-Skin-Cancer-using-Kalouche/b57ba909756462d812dc20fca157b3972bc1f533
[14]  Bisla, D., Choromanska, A., Stein, J.A., Polsky, D. and Berman, R. (2019) Towards Automated Melanoma Detection with Deep Learning: Data Purification and Augmentation. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Long Beach, 16-17 June 2019, 2720-2728.
http://arxiv.org/abs/1902.06061
[15]  Farag, A., Lu, L., Roth, H.R., Liu, J., Turkbey, E. and Summers, R.M. (2017) A Bottom-Up Approach for Pancreas Segmentation Using Cascaded Superpixels and (Deep) Image Patch Labeling. IEEE Transactions on Image Processing, 26, 386-399.
https://doi.org/10.1109/TIP.2016.2624198
[16]  Le, D.N., Le, H.X., Ngo, L.T. and Ngo, H.T. (2020) Transfer Learning with Classweighted and Focal Loss Function for Automatic Skin Cancer Classification. arXiv: 2009.05977.
[17]  Jafari, M.H., Karimi, N., Nasr-Esfahani, E., Samavi, S., Soroushmehr, S.M.R., Ward, K., et al. (2016) Skin Lesion Segmentation in Clinical Images Using Deep Learning. 2016 23rd International Conference on Pattern Recognition, Cancun, 4-8 December 2016, 337-342.
https://doi.org/10.1109/ICPR.2016.7899656
[18]  Tschandl, P., et al. (2019) Comparison of the Accuracy of Human Readers versus Machine-Learning Algorithms for Pigmented Skin Lesion Classification: An Open, Web-Based, International, Diagnostic Study. The Lancet Oncology, 20, 938-947.
https://doi.org/10.1016/S1470-2045(19)30333-X

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