# The Diagnostic Classification of the Pathological Image Using Computer Vision
## 4 Architectures, Features, and Advantages of CNNs
* SegNet: SegNet is a deep convolutional encoder-decoder architecture designed for semantic pixel-wise segmentation. An encoder-decoder architecture for semantic pixel-wise segmentation, which has been applied successfully to medical image analysis tasks [142]. There are some advantages: improved boundary delineation [143], memory efficiency, end-to-end training, efficient upsampling, and constant feature map size. SegNet's architecture enhances the accuracy of object boundaries in segmented images. The network uses a smaller number of parameters compared to other architectures, making it more memory-efficient during inference. SegNet can be trained end-to-end using stochastic gradient descent, which simplifies the training process. The use of max pooling indices for upsampling in the decoder network reduces the number of parameters and improves efficiency. SegNet maintains a constant number of features per layer, which decreases the computational cost for deeper encoder-decoder pairs. On the other hand, there are some limitations, such as performance trade-offs, limited contextual information, training complexity, dataset dependency, etc. While SegNet is efficient, some other architectures like DeepLab-LargeFOV may achieve higher accuracy at the cost of increased computational resources. The flat architecture of SegNet may capture less contextual information compared to networks with expanding deep encoder structures. Although end-to-end training is possible, SegNet may still require careful tuning of hyperparameters and training strategies to achieve optimal performance. The performance of SegNet can vary depending on the specific dataset and segmentation task, as demonstrated by comparisons at different benchmarks. SegNet's design prioritizes efficiency and practicality, making it suitable for applications with limited computational resources while still providing competitive segmentation performance. * EfficientNet: This is an improved version of ResNet and MobileNet, trained on low parameters while yielding excellent results for image classification [144; 145]. EfficientNet has shown promise in skin disease classification. An ensemble network, including EfficientNet, along with ResNet50 and MobileNetV3, has been proposed for classifying skin diseases. EfficientNet's main advantages are improved accuracy and efficiency through compound scaling and better performance with fewer parameters compared to other models. On the other hand, its limitations include that it may require careful tuning of hyperparameters and can be complex to implement and optimize. * Xception: This architecture is an extension of the Inception architecture that replaces Inception modules with depthwise separable convolutions, showing promise in medical image classification tasks. Xception, a deep learning model for image classification, offers several advantages: efficiency, improved accuracy, generalization, and parameter efficiency. It uses depthwise separable convolutions, reducing computational complexity and parameter count, leading to faster training and inference times. In addition, Xception's architecture enables better capture of spatial and cross-channel dependencies, resulting in state-of-the-art performance on various image classification benchmarks. Also, the model's design allows for better performance on unseen data, crucial for real-world applications. Further, Xception significantly reduces the number of parameters compared to traditional CNNs, making it more lightweight. However, Xception also has some limitations: greater memory requirements, more training data, and complexity. The depthwise separable convolutions consume more memory compared to traditional convolutions, which can be challenging in resource-constrained environments. Due to its higher number of parameters, Xception generally requires larger amounts of training data to achieve optimal performance.