Comparison between Convolutional Neural Network and Convolutional Neural Network-Support Vector Machines as the classifier for Colon Cancer

Aurelia, Jane Eva; Rustam, Zuherman; Wibowo, Velery Virgina Putri; Setiawan, Qisthina Syifa

doi:10.1109/dasa51403.2020.9317103

Cited by 4 publications

(2 citation statements)

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“…Many researchers have demonstrated deep learning efficiency as a feature extraction method in recent years (Kraus, Grys, Ba, Chong, Frey, Boone and Andrews, 2017). Moreover, many works in different tasks (Govindaswamy, Montague, Raicu and Furst, 2020), (Wang, Zhang and Hao, 2019), (Basly, Ouarda, Sayadi, Ouni and Alimi, 2020), (Alzubaidi, Fadhel, Al-Shamma, Zhang and Duan, 2020), (Aurelia, Rustam, Wibowo and Setiawan, 2020), (Mu and Qiao, 2019), (Suganthi and Sathiaseelan, 2020), (Oltu, Güney, Dengiz and Ağıldere, 2021), (Bodapati and Veeranjaneyulu, 2019), (Karungaru, Dongyang and Terada, 2021), (Öznur Özaltın and Özgür Yeniay, 2021) show the effectiveness of using ML classifier to classify the data based on features extracted through deep CNN compared to end-to-end deep learning.…”

Section: Introductionmentioning

confidence: 99%

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Multi-features extraction based on deep learning for skin lesion classification

2022

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For various forms of skin lesion, many different feature extraction methods have been investigated so far. Indeed, feature extraction is a crucial step in machine learning processes. In general, we can distinct handcrafted and deep learning features. In this paper, we investigate the efficiency of using 17 commonly pre-trained convolutional neural networks (CNN) architectures as feature extractors and of 24 machine learning classifiers to evaluate the classification of skin lesions from two different datasets: ISIC 2019 and PH2. In this research, we find out that a DenseNet201 combined with Fine KNN or Cubic SVM achieved the best results in accuracy (92.34% and 91.71% ) for the ISIC 2019 dataset. The results also show that the suggested method outperforms others approaches with an accuracy of 99% on the PH2 dataset.

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Section: Introductionmentioning

confidence: 99%

“…In (Aurelia et al, 2020), the authors proposed a research for the performance of Convolutional Neural Network, and Convolutional Neural Network-Support Vector Machine with several kernel functions for classification of colon cancer. The results obtained show the performance of CNN-SVM Linear Kernel with the best accuracy compared to CNN.…”

Section: Introductionmentioning

confidence: 99%