# The Diagnostic Classification of the Pathological Image Using Computer Vision
## 4 Architectures, Features, and Advantages of CNNs
This function ensures that the output values are in the range (0,1) and sum up to 1, making them interpretable as probabilities. The softmax function exponentiates each input value and then normalizes these values by dividing by the sum of all the exponentials, effectively creating a probability distribution over the classes. These formulas form the mathematical backbone of AlexNet, enabling it to process and classify images with remarkable accuracy. * VGG-16: This architecture is known for its simplicity and depth, with 16 weight layers. VGG-16 has proven effective in pathological image classification tasks. It achieves high accuracy in cataract detection, with a classification accuracy of 98.24%, sensitivity of 99.77%, and specificity of 97.83% when used as part of a larger architecture. Its deep structure allows for robust feature extraction, making it suitable for complex pathological image analysis. However, VGG-16 has limitations: (i) a large number of parameters, leading to high computational costs, (ii) potential for overfitting on smaller datasets, and (iii) limited ability to capture multi-scale features compared to more modern architectures. * ResNet: This architecture introduced residual connections to train very deep networks, with some versions exceeding 100 layers. ResNet's key advantage is its ability to train very deep networks effectively, mitigating the vanishing gradient problem through skip connections [139]. This allows for improved performance in complex image recognition tasks, including pathological image analysis. The advantages facilitate the training of extremely deep networks effective in handling complex image recognition tasks and are computationally efficient compared to some other architectures. On the other hand, limitations include that it may not be as efficient in feature reuse as DenseNet and can require more parameters to achieve similar performance to DenseNet. * Inception (GoogLeNet): This architecture features inception modules for efficient feature extraction. Inception architectures, like Inception-v3 and Inception-v4, are designed to be computationally efficient while maintaining high accuracy [140]. The advantages are computationally efficient, flexible in handling different scales of features, and performs well even with limited training data. On the other hand, as for limitations, complex architecture can be challenging to modify or interpret and may suffer from overfitting if not properly tuned. * DenseNet: This architecture features dense connections between layers, allowing for improved information flow and gradient propagation [141]. It has shown effectiveness in various medical imaging tasks, including classification and segmentation [142]. DenseNet excels in feature reuse and parameter efficiency, making it particularly effective for tasks requiring fine-grained feature analysis [140]. The advantage is highly parameter-efficient, achieving high accuracy with fewer parameters, excellent feature reuse across the network, and resilience to vanishing gradient problems. On the other hand, its limitations are higher memory usage due to feature map concatenation and that it can be computationally intensive, especially for very deep versions. * U-Net: Specifically designed for biomedical image segmentation, U-Net features a contracting path to capture context and a symmetric expanding path for precise localization [142]. U-Net excels in medical image segmentation tasks, particularly in identifying structures in pathological images. When combined with attention mechanisms, such as in the Deep Attention U-Net for Cataract Diagnosis (DAUCD) model, it achieves impressive results in blood vessel segmentation for cataract detection. Advantages of U-Net include efficient use of context information, ability to work with limited training data, and good performance in biomedical image segmentation. On the other hand, limitations may include its struggle with very small or highly imbalanced datasets and can be computationally intensive for large images.