Table 12.
Comparison with existing state-of-art models.
| Study | Dataset source | No. of images | Technique used | Accuracy (%) |
|---|---|---|---|---|
| Boldú et al. [1] | ImageNet | 16450 | DenseNet121 | 93.6 |
| Baby and Devaraj [2] | ImageNet | 16450 | VGG16 | 82.35 |
| Yao et al. [3] | Kaggle | 12444 | VGG16 | 95.7 |
| Sen et al. [4] | HospitalSantiago de cube | 626 | InceptionV3 | 91 |
| Sheng et al. [7] | MS COCO | 1673 | ResNet50 | 75.71 |
| Patil et al. [8] | Kaggle | 12444 | Xception + LSTM | 95.89 |
| Özyurt [9] | Kaggle | 12444 | AlexNet | 95.29 |
| Acevedo et al. [10] | Hospital clinic of barcelona | 17092 | VGG16 | 96.2 |
| Sharma et al. [11] | Kaggle | 12444 | LeNet | 87.93 |
| Huang et al. [12] | LCTFS | 10000 | MGCNN | 97.65 |
| Proposed methodology | Kaggle | 12444 | DenseNet121 | 98.84 |