Yurui Li
ABSTRACT
Skin cancer is one of the most threatening types of cancer and has been rising over the past decade. Traditional skin detection methods can be time-consuming and inefficient. Convolutional neural network (CNN) is a powerful autonomous feature extraction method with high accuracy in diagnosing skin cancer. However, most existing skin cancer detection approaches based on CNN only consider a single model and lack intelligent communication. To address this problem, this paper proposes a skin cancer detection system using three CNNs to provide fast, accurate, intelligent, and diversified detection and provide feedback. The three CNNs used in this paper are VGG16, MobileNet and Inception_resnet_v2. Our System consists of three main components, Skin Image Analysis Module, Medical Chatbot Module, and UI Web Module. In Skin Image Analysis Module, we implement the skin cancer detection function of two types: classifying malign moles and benign moles and classifying 7 classes of Skin lesions. In Medical Chatbot Module, the function of online consultation between users and virtual doctors is realized. In UI Web Module, we provide functionalities including uploading their image of moles for detection. VGG16, MobileNet, and Inception_resnet_v2's accuracies and transfer learning techniques are used on ISIC, and HAM1000 datasets are 0.88, 0.90, and 0.93, respectively. Our system can allow users to upload an image of their mole, chat with a virtual assistant, and receive timely and reliable test results, thereby improving the survival rate of potential patients.
- Abuared, N., Panthakkan, A., Al-Saad, M., Amin, S.A. and Mansoor, W. (2020) Skin Cancer Classification Model Based on VGG 19 and Transfer Learning. In: 2020 3rd International Conference on Signal Processing and Information Security (ICSPIS). pp. 1--4.Google Scholar
Cross Ref
- Abuared, N., Panthakkan, A., Al-Saad, M., Amin, S.A. and Mansoor, W. (2020) Deep Convolutional Neural Network (DCNN) for Skin Cancer Classification. In: 2020 27th IEEE International Conference on Electronics, Circuits and Systems (ICECS). pp. 1--4.Google ScholarCross Ref
- Sedigh, P., Sadeghian, R. and Masouleh, M.T. (2019) Generating Synthetic Medical Images by Using GAN to Improve CNN Performance in Skin Cancer Classification. In: 2019 7th International Conference on Robotics and Mechatronics (ICRoM). pp. 497--502.Google ScholarCross Ref
- Tschandl, P., Rosendahl, C. and Kittler, K. (2018) The ham10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Scientific data, 5(1): 1--9.Google Scholar
- Datta, S.K, Shaikh, M.A., Srihari, S.N., et al. (2021) Soft-Attention Improves Skin Cancer Classification Performance. arXiv preprint arXiv: 2105.03358.Google Scholar
- Szegedy, C., Ioffe, S., Vanhoucke, V. and Alemi, A. (2016) Inception-v4, inception-resnet and the impact of residual connections on learning. arXiv preprint arXiv:1602.07261.Google Scholar
- Tran, D., Bourdev, L., Fergus, R., Torresani, L. and Paluri, M. (2015) Learning spatiotemporal features with 3d convolutional networks. In: Proceedings of the IEEE international conference on computer vision. pp. 4489--4497.Google ScholarDigital Library
- Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I. and Salakhutdinov, R. (2014) Dropout: a simple way to prevent neural networks from overfitting. The journal of machine learning research. 15(1):1929--1958.Google Scholar
- Parul, P. (2021) Building a Simple Chatbot from Scratch in Python (Using NLTK). In: Medium, Analytics Vidhya.Google Scholar
- Kingma, D.P. and Adam, J.B. (2014) A method for stochastic optimization. arXiv preprint arXiv:1412.6980.Google Scholar
- Ioffe, S. and Szegedy, C. (2015) Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167.Google ScholarDigital Library
Index Terms
- Intelligent Skin Cancer Detection System Based on Convolutional Neural Networks
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