Table 10 Results comparison with previous study.
From: A deep ensemble learning approach for squamous cell classification in cervical cancer
Reference | Method | Dataset | Results |
|---|---|---|---|
Promworn et al.30 | Comparative analysis of models | N/A | DenseNet161 achieved 94.38% acc. |
ColpoNet15 | Inspired by DenseNet | Nat. Cancer Institute dataset | Accuracy of 81.353% |
Parikshit Sanyal et al.16 | CNN for detecting ’abnormal’ foci | 1838 microphotographs | 95.46% diagnosis acc. |
Karunakaran et al.17 | Ultrasensitive SERS for sample prediction | Cervix cell samples | Average acc. of 95.46% |
Kudva et al.19 | Hybrid transfer learning system | AlexNet and VGG-16 features | Classification acc. of 91.46% |
Ghoneim et al.22 | CNN-based approaches with ELM classifiers | Herlev database | 99.5% detection acc. and 91.2% classification acc. |
Kang et al.25 | Raman spectroscopy, H-CNN | Tissue samples | Over 94% accuracy in classifying tissues |
Proposed Method | SipakMed | Ensemble Model | Overall accuracy is 94% |
