Table 2 The detailed summary of the previous models used for leukemia classification.
Deep CNN | Neurons | Limitations |
|---|---|---|
VGG16 | 33 × 106 | This model is slow in training and computationally expensive due to many trainable parameters |
AlexNet | 24 × 106 | Due to the large number of trainable neurons, AlexNet is also costly. Moreover, the model is unable to detect all high-dimensional spatial features |
ResNetXt | 23 × 106 | ResNeXt is a fifty-layer deep CNN model that can extract high-dimensional features that require a large training dataset. In addition, it cannot be used for real-time applications due to the significant number of trainable parameters |
DenseNet-121 | 7.2 × 106 | DenseNet-121 has significantly less trainable parameters. However, this model's performance is less compared to other state-of-the-art models |
