The week's pick
Random Articles
Reseach Article
Skin Cancer Classification using VGG-16 and Googlenet CNN Models
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 184 - Number 42 |
| Year of Publication: 2023 |
| Authors: Suryakanth B. Ummapure, Ravindrakumar Tilekar, Satishkumar Mallappa |
10.5120/ijca2023922497
|
Suryakanth B. Ummapure, Ravindrakumar Tilekar, Satishkumar Mallappa . Skin Cancer Classification using VGG-16 and Googlenet CNN Models. International Journal of Computer Applications. 184, 42 ( Jan 2023), 5-9. DOI=10.5120/ijca2023922497
Abstract
Skin cancer with a high fatality rate is called melanoma. Due to the great degree of similarities among the many forms of skin lesions, a proper diagnosis cannot be made. Dermatologists can treat patients and save their lives by accurately classifying skin lesions in their early stages. This paper proposes a model for highly accurate skin lesion classification. The proposed model made use of transfer learning models known as GoogleNet and vgg16. This model efficiently distinguished between benign and malignant cancerous skin lesions, those are the two distinct classes of skin diseases. The 1800 benign cancer images and 1498 malignant cancer images that were retrieved from the internet were taken into account for this proposed strategy. The VGG16 has obtained the highest recognition accuracy in the result accessing, with recognition rates of 99.62% for training and 84.97% for validation.
References
- Jinnai, S., Yamazaki, N., Hirano, Y., Sugawara, Y., Ohe, Y., & Hamamoto, R. (2020). The development of a skin cancer classification system for pigmented skin lesions using deep learning. Biomolecules, 10(8), 1123.
- Le, D. N., Le, H. X., Ngo, L. T., & Ngo, H. T. (2020). Transfer learning with class-weighted and focal loss function for automatic skin cancer classification. arXiv preprint arXiv:2009.05977.
- Md Shahin Ali, Md Sipon Miah, Jahurul Haque, Md Mahbubur Rahman, Md Khairul Islam,” An enhanced technique of skin cancer classification using deep convolutional neural network with transfer learning models”, Machine Learning with Applications 5 (2021).
- Jinnai, Shunichi, et al. "The development of a skin cancer classification system for pigmented skin lesions using deep learning." Biomolecules 10.8 (2020): 1123.
- Amin, Javeria, et al. "Integrated design of deep features fusion for localization and classification of skin cancer." Pattern Recognition Letters 131 (2020): 63-70.
- Chaturvedi, Saket S., Kajol Gupta, and Prakash 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. Springer, Singapore, 2020.
- Manne, Ravi, Snigdha Kantheti, and Sneha Kantheti. "Classification of Skin cancer using deep learning, ConvolutionalNeural Networks-Opportunities and vulnerabilities-A systematic Review." International Journal for Modern Trends in Science and Technology, ISSN (2020): 2455-3778.
- Thomas, Simon M., et al. "Interpretable deep learning systems for multi-class segmentation and classification of non-melanoma skin cancer." Medical Image Analysis 68 (2021): 101915.
- Filali, Youssef, et al. "Efficient fusion of handcrafted and pre-trained CNNs features to classify melanoma skin cancer." Multimedia Tools and Applications 79.41 (2020): 31219-31238.
- Singh, Lokesh, Rekh Ram Janghel, and Satya Prakash Sahu. "TrCSVM: a novel approach for the classification of melanoma skin cancer using transfer learning." Data Technologies and Applications (2020).
- Szegedy, Christian, et al. "Going deeper with convolutions." Proceedings of the IEEE conference on computer vision and pattern recognition. 2015
- Simonyan, Karen, and Andrew Zisserman. "Very deep convolutional networks for large-scale image recognition." arXiv preprint arXiv:1409.1556 (2014).
- https://www.kaggle.com/datasets/fanconic/skin-cancer-malignant-vs-benign
Index Terms
Keywords