An Improved Deep CNN For an Early and Accurate Skin Cancer Detection and Diagnosis System
Abstract
Skin cancer is considered to be the most common and dangerous type of cancer. Information technology techniques are required to detect and diagnose skin cancer. Therefore, there is a need for an early and accurate skin cancer diagnosis and detection by employing an efficient deep learning technique. This research work proposes automatic diagnosis of skin cancer by employing Deep Convolution Neural Network (DCNN). The distinguishing feature of this research is it employs DCNN with 12 nested processing layers increasing the diagnosis and detection of skin cancer accuracy. Beside neural network, machine learning techniques of naïve Bayes and random forest are also utilized to detect skin cancer. This research work results concluded that the deep learning technique are more effective than machine learning in terms of skin cancer detection. By applying Naïve Bayesian on the proposed system accuracy of 96% were achieved, similarly for Random Forest method, an accuracy of 97% were achieved. The accuracy of 99.5% were achieved by applying Deep CNN network. The performance of proposed system has been compared with other research work and it is concluded that it shows the higher performance compared to all conventional systems.
Keywords
Skin Cancer, Machine Learning, Deep learning and Detection, Diagnosis.
References
- Boman, J., & Volminger, A. (2018). Evaluating a deep convolutional neural network for classification of skin cancer. In.
- Bray, F., Ferlay, J., Soerjomataram, I., Siegel, R. L., Torre, L. A., & Jemal, A. (2018). Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: a cancer journal for clinicians, 68(6), 394-424.
- Brekhna, B., Mahmood, A., Zhou, Y., & Zhang, C. (2017). Robustness analysis of superpixel algorithms to image blur, additive Gaussian noise, and impulse noise. Journal of Electronic Imaging, 26(6), 061604.
- Daghrir, J., Tlig, L., Bouchouicha, M., & Sayadi, M. (2020). Melanoma skin cancer detection using deep learning and classical machine learning techniques: A hybrid approach. Paper presented at the 2020 5th international conference on advanced technologies for signal and image processing (ATSIP).
- Dorj, U.-O., Lee, K.-K., Choi, J.-Y., & Lee, M. (2018). The skin cancer classification using deep convolutional neural network. Multimedia Tools and Applications, 77(8), 9909-9924. Gedraite, E. S., & Hadad, M. (2011). Investigation on the effect of a Gaussian Blur in image filtering and segmentation. Paper presented at the Proceedings ELMAR-2011.
- Grigoryan, A. M., & Agaian, S. S. (2020). New look on quantum representation of images: Fourier transform representation. Quantum Information Processing, 19(5), 1-26.
- Kadampur, M. A., & Al Riyaee, S. (2020). Skin cancer detection: Applying a deep learning based model driven architecture in the cloud for classifying dermal cell images. Informatics in Medicine Unlocked, 18, 100282.
- Krive, J., Patel, M., Gehm, L., Mackey, M., Kulstad, E., Lussier, Y. A., & Boyd, A. D. (2015). The complexity and challenges of the international classification of diseases, ninth revision, clinical modification to international classification of diseases, 10th revision, clinical modification transition in eds. The American journal of emergency medicine, 33(5), 713-718.
- Ku, J., Harakeh, A., & Waslander, S. L. (2018). In defense of classical image processing: Fast depth completion on the cpu. Paper presented at the 2018 15th Conference on Computer and Robot Vision (CRV).
- Kumar, M., Alshehri, M., AlGhamdi, R., Sharma, P., & Deep, V. (2020). A de-ann inspired skin cancer detection approach using fuzzy c-means clustering. Mobile Networks and Applications, 25(4), 1319-1329.