Efficient fusion of handcrafted and pre-trained CNNs features to classify melanoma skin cancer
- PDF / 939,479 Bytes
- 20 Pages / 439.37 x 666.142 pts Page_size
- 55 Downloads / 160 Views
Efficient fusion of handcrafted and pre-trained CNNs features to classify melanoma skin cancer Youssef Filali 1
1
1
& Hasnae EL Khoukhi & My Abdelouahed Sabri & Abdellah Aarab
2
Received: 2 November 2019 / Revised: 10 July 2020 / Accepted: 13 August 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract
Skin cancer is one of the most aggressive cancers in the world. Computer-Aided Diagnosis (CAD) system for cancer detection and classification is a top-rated solution that decreases human effort and time with very high classification accuracy. Machine learning (ML) and deep learning (DL) based approaches have been widely used to develop robust skin-lesion classification systems. Each of the techniques excels when the other fails. Their performances are closely related to the size of the learning dataset. Thus, approaches that are based on the ML are less potent than those found on the DL when working with large datasets and vice versa. In this article, we propose a powerful skin-lesion classification approach based on a fusion of handcrafted features (shape, skeleton, color, and texture) and features extracted from most powerful DL architectures. This combination will make it possible to remedy the limitations of both the ML and DL approaches for the case of large and small datasets. Features engineering is then applied to remove redundant features and to select only relevant features. The proposed approach is validated and tested on both small and large datasets. A comparative study is also conducted to compare the proposed approach with different and recent approaches applied to each dataset. The results obtained show that this features-fusion based approach is very promising and can effectively combine the power of ML and DL based approaches. Keywords Skin cancer . Melanoma . Handcrafted features . CNNs . Features fusion . Genetic algorithm
1 Introduction The rate of skin cancer incidence has been increasing in the last decades. The reduction of the ozone layer that protects the human body from the radiation in part, the abusive body * Youssef Filali [email protected]
1
Department of Computer science, Faculty of Sciences Dhar-Mahraz, USMBA, Fez, Morocco
2
Department of Physics, Faculty of Sciences Dhar-Mahraz, USMBA, Fez, Morocco
Multimedia Tools and Applications
exposition to the sun, or the use of the tanning are an explanation of the present trend all over the world. The difficulty in distinguishing between melanoma and non-melanoma skin cancer pushes many medical communities to invest money, time, and effort to raise awareness of the danger that presents the actual type of cancer. However, it is also important to invest in the development of techniques that can be used in the early prevention of this cancer. There are different techniques; one of them is the acquisition of the image of skin lesion. It can be acquired from either macroscopic or dermoscopic devices. The macroscopic, which are called clinical images, are taken from mobile phones or standard cameras. Th
Data Loading...
