# The Diagnostic Classification of the Pathological Image Using Computer Vision
## 4 Architectures, Features, and Advantages of CNNs
While more efficient than some earlier models, Xception's architecture is still complex, which can make it challenging to implement and optimize in certain scenarios [146]. * Custom CNN architectures: Researchers have developed task-specific CNN models for medical imaging. For example, a study proposed two simplified CNN models for Alzheimer's disease classification using MRI data, achieving high accuracy with a straightforward structure [147]. Custom CNNs have been successfully applied in various pathological tasks. For instance, the Mask Cell of the multi-class deep network (MCNet) achieves high accuracy in blood cell detection and classification, with a [EMAIL_ADDRESS] of 95.70 for the PBC dataset and 96.76 for the Blood Cell count and Detection (BCCD) dataset. The advantages of custom CNNs are that they can be tailored to specific pathological tasks, with flexibility in architecture design and the potential for high performance when optimized. On the other hand, the limitations require significant expertise to design and optimize and may not generalize well to other tasks without modification. These architectures offer various advantages, such as improved efficiency, better feature extraction, and specialized capabilities for specific medical imaging tasks. The choice of architecture often depends on the specific requirements of the medical image processing task at hand [148]. When comparing these architectures, it is important to consider the specific pathological task. For classification tasks (e.g., tumor pathology prediction), VGG-16 and EfficientNet have shown strong performance. For segmentation tasks (e.g., blood vessel segmentation in retinal images), U-Net and its variants like DAUCD have demonstrated excellent results. For object detection and instance segmentation (e.g., blood cell detection), custom architectures like MCNet have proven effective. For tasks with limited labeled data, semi-supervised learning (SSL) approaches have shown promise, achieving comparable performance to fully supervised models while significantly reducing the need for annotations [148]. Thus, the choice of architecture depends on the specific pathological task, available data, and computational resources. Hybrid approaches, such as combining different architectures or using ensemble methods, often yield the best results in complex pathological image analysis tasks. CNNs offer several significant advantages that have made them a powerful and widely used tool in machine learning, particularly for image and video processing tasks. CNNs excel at automatically learning and extracting relevant features from input data, especially images. Their convolutional layers apply filters that capture spatial dependencies and patterns, eliminating the need for manual feature engineering. This allows CNNs to discover salient characteristics like edges, shapes, colors, and textures directly from raw pixel data. The architecture of CNNs provides spatial invariance, allowing them to recognize objects and patterns regardless of their position or orientation within an image [149]. This makes CNNs robust to variations in input data. CNNs build increasingly abstract and high-level representations as they process data through deeper layers [150]. This hierarchical approach enables them to capture complex features and relationships in visual data. CNNs use parameter sharing, which significantly reduces the number of parameters compared to fully connected networks. This makes CNNs more computationally efficient and helps prevent overfitting. Pre-trained CNN models can be fine-tuned for specific tasks, enabling effective transfer learning [151]. This allows CNNs to leverage knowledge gained from large datasets to perform well on smaller, specialized datasets. While CNNs are renowned for their performance in computer vision tasks, their benefits extend to other domains. CNNs have shown promise in text analysis tasks like sentiment analysis, topic categorization, and language translation [141, 152].