Table 3 Comparison between different studies of classifications
From: Automated detection of colon cancer using genomic signal processing
Study | Data set | Method | Classifier | Output |
|---|---|---|---|---|
Mendizabal-Ruiz et al. [5] | DNA sequence | Voss representation DFT PSD | K-means algorithm | Clustering |
Ali et al. [7] | WBCD | MLP | ACC = 96.7% | |
KNN | ACC = 96.27% | |||
CART | ACC = 91% | |||
NB | ACC = 93.62% | |||
SVM | ACC = 96.42% | |||
Indu et al. [10] | DNA Microarray | Integrating correlation-based feature selection model | iBPSO | ACC about 92% |
Fang et al. [15] | Ultrasound (US) images | Region of interest extraction based on SLIC | SLIC | ACC up to 92.05% |
Zhou et al. [17] | Magnetic resonance imaging (MRI) images | Localizing the lesions at dynamic contrast-enhanced MRI data in a weakly supervised manner | CNN | ACC = 95% |
Lakshmanaprabu et al. [20] | Computed tomography (CT) images | The deep features extracted from a CT lung images | ODNN | ACC = 94.56% |
Presented Study | DNA sequences: | DWT Statistical features | KNN | ACC = 97.5% |
SVM | ACC = 95% |