Skin Cancer Detection Based on Hybrid Model by Means of Inception V3 and ResNet 50

DOI

10.2991/978-94-6463-040-4_42How to use a DOI?

Keywords

Skin Cancer Classification; Deep Convolution Neural Network; InceptionV3; ResNet50; Feature Fusion; NLP; iOS App

Abstract

Due to the rapid growth of skin cancer patients, being diagnosed and treated at an early stage has become more and more necessary. However, only mature, and experienced doctors are capable for the precise detection of skin cancer by carrying out some expensive examination. To solve this issue, several computer-based, deep-learning detection methods have been proposed over recent years. However, most of current methods only focus on algorithms and do not provide with industrial applications. In addition, the current accuracy based on these algorithms can be further improved. This paper presents an improved deep Convolutional Neural Network (CNN) model of feature fusion by merging InceptionV3 and ResNet50, to classify images of skin cancer into eight types. By concatenating, the proposed model has not only the Inception Modules, but also the Residual Blocks, which obtains the advantages of reducing the number of parameters and mitigating the gradient problem. The experiment has been carried out on the augmented HAM10000 dataset, which contains quite several images of different types of skin cancer. The experimental results show that the proposed model has the best training performance with a validation accuracy at 87.11% among InceptionV3, ResNet50, VGG19 and itself. Besides, during prediction, the proposed model also holds the best achievement with accuracy at 0.822, precision at 0.824, recall at 0.822, and F1-score at 0.817 among the four models, which has a rivalrous performance than VGG19. Furthermore, an iOS App was also developed to provide a better interactive experience for users to acquire a diagnosis through one single image of skin lesion and communicate about their results conveniently.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Yuyu Zeng
AU  - Xingsheng Zhu
PY  - 2022
DA  - 2022/12/27
TI  - Skin Cancer Detection Based on Hybrid Model by Means of Inception V3 and ResNet 50
BT  - Proceedings of the 2022 3rd International Conference on Artificial Intelligence and Education (IC-ICAIE 2022)
PB  - Atlantis Press
SP  - 275
EP  - 282
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-040-4_42
DO  - 10.2991/978-94-6463-040-4_42
ID  - Zeng2022
ER  -