# The Diagnostic Classification of the Pathological Image Using Computer Vision
## 2 Applications of Deep Learning Approaches for Diagnostic Classification
AutoML models have been applied to predict liver metastasis in patients with gastrointestinal stromal tumors. These models utilize algorithms like Gradient Boosting Machine (GBM) to provide clinicians with tools for individualized treatment planning, demonstrating high accuracy in predicting liver metastasis [40]. Deep learning models using CNNs have been proposed for the automatic detection of liver cancer, transferring knowledge from pre-trained global models to improve diagnostic accuracy [41]. These models are designed to handle various imaging modalities and clinical data, enhancing their applicability in clinical settings. Deep learning models are proving to be valuable tools in the early detection and diagnosis of liver and gastrointestinal cancers, offering high accuracy and efficiency that can complement traditional diagnostic methods. In addition, deep learning models have shown impressive performance in detecting and classifying liver tumors. CNNs applied to B-mode ultrasound images achieved higher accuracy than expert radiologists in distinguishing benign from malignant focal liver lesions [42]. A CNN model for liver tumor detection and 6-class discrimination (including hepatocellular carcinoma, hemangiomas, and cysts) reached 87% detection rate, 83.9% sensitivity, and 97.1% specificity in internal evaluation [42]. Deep learning systems integrating unenhanced MRI images and clinical data demonstrated excellent performance in classifying liver malignancies, with AUCs of 0.985 for hepatocellular carcinoma, 0.998 for metastatic tumors, and 0.963 for other primary malignancies [43]. Deep learning has also shown promise for other gastrointestinal cancers. For gastric cancer, a combined AI model incorporating handcrafted radiomics and deep learning radiomics achieved an AUC of 0.786 and an accuracy of 71.6% for diagnosing signet ring cell carcinoma [44]. In colorectal cancer, deep learning models have been developed for tasks like polyp detection and classification during colonoscopy, enhancing early detection capabilities. The application of deep learning offers several benefits. Many deep learning models achieve performance on par with or exceeding that of experienced radiologists [39]. AI models can rapidly analyze large volumes of imaging data, potentially reducing diagnostic time [40]. Deep learning models have indeed revolutionized the analysis of medical imaging data, offering significant potential to reduce diagnostic time and improve accuracy [45]. Deep learning models, particularly CNNs, excel at automatically extracting relevant features from medical images [46]. This capability eliminates the need for time-consuming manual feature engineering, allowing for rapid analysis of large datasets. Once trained, deep learning models can process and analyze images at high speeds, enabling them to handle large imaging data much faster than human radiologists. This high-throughput capability is especially valuable in screening applications or in situations where quick turnaround times are critical. Advanced deep learning architectures can integrate and analyze data from multiple imaging modalities simultaneously, providing a more comprehensive view of a patient's condition [47]. This ability to synthesize information from various sources can lead to more accurate and timely diagnoses. Deep learning models can rapidly screen large numbers of images to identify potential abnormalities, allowing radiologists to focus on cases requiring closer examination. This approach can significantly reduce the time needed to process large screening datasets, such as in mammography or lung cancer screening [48; 49]. Deep learning models can help prioritize urgent cases by quickly analyzing incoming imaging studies, ensuring that critical conditions receive prompt attention. This triage capability can lead to faster diagnoses for time-sensitive conditions. Deep learning methods can perform comprehensive quantifications of tissue characteristics much faster than manual methods.