Table 6 Explain how features can be used in different models.
Model | Main Features | Use in DenseNet121 | Use in InceptionV3 |
|---|---|---|---|
ResNet-18 | − 18 layers with skip connections (residual blocks). | DenseNet121 can leverage residual blocks to enhance feature propagation between dense layers. | InceptionV3 can utilize residual features for multi-scale feature extraction at different levels. |
- Efficient for tasks requiring less computational power. | Useful for smaller datasets where DenseNet121’s dense blocks can combine these residuals. | InceptionV3 can integrate residual blocks to process features across multiple inception modules. | |
MobileNetV2 | - Depthwise separable convolutions for efficiency. | DenseNet121 can utilize efficient MobileNetV2 features for low-latency applications. | InceptionV3 can enhance its depthwise operations using MobileNetV2 for lightweight feature extraction. |
- Bottleneck layers with inverted residuals. | Bottleneck features can be reused across DenseNet121’s dense blocks for better propagation. | InceptionV3 can use inverted residuals to improve feature extraction in its branching structure. | |
ResNet-50 | − 50 layers with deeper residual blocks. | DenseNet121 can combine ResNet-50’s deep residual features to create dense connections. | InceptionV3 can exploit ResNet-50’s deep residuals to enhance multi-scale feature processing. |
- Suitable for deeper and more complex feature extraction. | DenseNet121 can add these deeper features to its own block connections for improved accuracy. | InceptionV3’s complex inception blocks can integrate these deeper features for high-accuracy tasks. | |
EfficientNet | - Compound scaling of depth, width, and resolution. | DenseNet121 can use EfficientNet’s compound scaling features to maintain a balance between accuracy and efficiency. | InceptionV3 can use EfficientNet’s scalable features to optimize its inception blocks for different computational needs. |
- State-of-the-art accuracy with fewer parameters. | DenseNet121 can adapt these efficient features to improve performance without increasing complexity. | InceptionV3 can incorporate EfficientNet’s features for balanced and scalable image processing tasks. |
