# The Diagnostic Classification of the Pathological Image Using Computer Vision
## 4 Architectures, Features, and Advantages of CNNs
For instance, a study demonstrated that low-resolution thumbnail images could classify slide types with high accuracy (AUROC > 0.99), making vast pathology archives more accessible for AI applications [163]. Also, there are some quality assurance measures, such as image quality control, representation of the target population, data augmentation, and diversity and bias mitigation. Automated quality assessment tools, such as PathProfiler, evaluate WSIs for usability at a diagnostic level by detecting common image artifacts (e.g., focus issues or staining quality). These tools ensure that only high-quality images are included in datasets, minimizing biases introduced by poor-quality slides [164]. Test datasets are curated to represent the entire target population of images encountered in real-world scenarios. This involves covering all dimensions of biological and technical variability (e.g., tissue features or staining protocols) to ensure unbiased model evaluation and prevent shortcut learning [165]. Generative adversarial networks (GANs) have been utilized for data augmentation to enhance dataset diversity, improving model performance in histopathological image classification by simulating realistic variations in pathological features [166]. Efforts are made to include diverse cases across multiple dimensions (e.g., tissue types or disease states) while addressing low-prevalence subsets to minimize spurious correlations between confounding variables and target outcomes [165]. Thus, by employing these strategies, researchers aim to create robust datasets that facilitate reliable training and evaluation of AI models for diagnostic applications in pathology. A Comprehensive Comparison of CNN Models and State-of-the-Art Methods for Images in Different Modalities for Different Diseases  
CNNs have become a powerful tool for analyzing medical images across various modalities and diseases. The quantitative evaluation of these models is crucial for assessing their performance and reliability in clinical settings. Common metrics used for the quantitative assessment of CNN models in medical image analysis include the Structural Similarity Index (SSIM), which measures the similarity between the original and processed images, Peak Signal-to-Noise Ratio (PSNR), which assesses the quality of denoised or reconstructed images, Precision and Recall, which evaluate the model's detection performance, Coefficient of Determination (R\({}^{2}\)), which measures the goodness of fit between predicted and ground truth values, and Mean Absolute Deviation (MAD), which quantifies the error between estimated and true values while being less sensitive to outliers than RMSD [167; 168]. As applications in different modalities, CNNs have shown significant improvements in denoising low-dose CT images, especially at ultra-low-dose levels. These models can enhance image quality while maintaining sharpness, with SSIM improvements of approximately 10% compared to the original methods [167]. Additionally, [PERSON]'s disease classification using MRI data has developed a quantitative disease-focusing approach. This method combines saliency maps and brain segmentations to evaluate how well CNN models focus on [PERSON]'s disease-relevant brain regions [169]. Also, a deep learning model has been proposed for quantitatively assessing chest X-ray findings with varying annotations. This model provides a quantitative evaluation of the probability of findings, which is proportional to their obviousness [170]. Further, CNN-based pipelines have been developed to analyze cell culture images acquired by lens-free microscopy. These models can perform multi-parameter analysis of ~10,000 cells in less than 5 s, demonstrating efficiency in bio-imaging analysis [168]. When comparing different CNN architectures, cross-validation is used to assess the reproducibility of classification outputs [170]. The model outputs are compared with the consistency of findings from multiple physicians [170].