Table 7 Comparison with State-of-art.
From: Deep learning model for early acute lymphoblastic leukemia detection using microscopic images
References | Technique | Dataset / No. of images | Accuracy (%) |
|---|---|---|---|
Bayesian-based CNN | ALL-IDB / 368 | 93.5 | |
Efficient channel attention + VGG16 | C-NMC2019 / 7272 | 91.1 | |
ALLNET | C-NMC2019 / 7272 | 95.54 | |
VGG16 | CodaLab / 8491 | 84.62 | |
82 | 19 layer CNN | Public / 293 | 93.18 |
IoT Model | ALL-IDB / 179 | 95.5 | |
EfficientB0 | Public / 3242 | 72 | |
DarkNet19 ESA | Public / 3256 | 98.52 | |
SVM | ALL-IDB / 260 | 97.4 | |
ResNet50 | ALL-PBS / 3242 | 99.38 | |
MobileNetV2 | ALL-PBS / 3256 | 97.4 | |
CNN | C-NMC2019 / 7272 | 93.9 | |
Ensemble-ALL | C-NMC2019 / 7272 | 96.26 | |
Deep Dilated CNN | Public / 362 | 91.98 | |
Proposed | Adam Optimized Deep CNN | Acute Lymphoblastic Leukemia | 96.00 |
