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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

has been cited by the following article:

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

  AUTHORS: Adam M. Ibrahim, Mohammed Elbasheir, Somia Badawi, Ashraf Mohammed, Amir F. Mohammed Alalmin

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

  JOURNAL NAME: Journal of Intelligent Learning Systems and Applications, Vol.15 No.3, August 8, 2023

  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.