# The Diagnostic Classification of the Pathological Image Using Computer Vision
## 1 Introduction  
Deep learning models, particularly convolutional neural networks (CNNs), have shown remarkable performance in classifying pathological images. Deep learning-based approaches have shown significant promise for the diagnostic classification of pathological images, particularly whole-slide images (WSIs) [1, 2, 3, 4]. Pre-trained CNN architectures like DenseNet-161 and ResNet-50, have achieved classification accuracies of over 97% on histopathology image datasets. These models can automatically extract relevant features from WSIs to classify them into different disease categories. CNNs have emerged as the primary deep learning architecture for pathological image classification tasks. Several CNN models have demonstrated impressive performance. With high accuracy, the Inception-v3 architecture has been used to classify epithelial tumors of the stomach and colon into adenocarcinoma, adenoma, and non-neoplastic categories. Models like ResNet50, ResNeXt50, EfficientNet, and DenseNet121 have been applied to diagnose different types of thyroid tumors [5]. GoogleNet and AlexNet have also shown good classification accuracy for pathological images. Deep learning models have been successfully applied to various cancertypes. In prostate cancer, artificial intelligence (AI)-based models have been developed to classify prostate Magnetic Resonance Imaging (MRI) images, potentially reducing the need for invasive biopsies [6]. In melanoma, a CNN outperformed 11 histopathologists in classifying histopathological melanoma images, showing promise in assisting human melanoma diagnosis [7]. In thyroid tumors, deep neural networks have shown value in the pathological classification of thyroid tumors [5]. WSIs present unique challenges due to their extremely large size. To address this, researchers have developed novel approaches. (I) HipoMap: A framework that creates a WSIs representation map that can be applied to various slide-based problems. It outperformed existing methods in lung cancer classification experiments, achieving an Area Under the Curve (AUC) of 0.96 \(\pm\) 0.026 [8]. (II) Tile-based approaches: CNNs are trained on millions of tiles extracted from WSIs, with predictions aggregated using strategies like max pooling or recurrent neural networks (RNNs) [9]. Deep learning models have demonstrated the ability to simultaneously detect and classify multiple pathological findings. For example, models have been developed to classify seven different histopathological findings in rat liver WSIs, including vacuolation, bile duct hyperplasia, and single-cell necrosis [10]. In gastric and colonic epithelial tumors, models have been trained to differentiate between adenocarcinoma, adenoma, and non-neoplastic lesions [10]. Transfer learning techniques have been successfully applied to adapt pre-trained CNN models for pathology image classification tasks [11]. This approach allows leveraging knowledge gained from large natural image datasets to improve performance on smaller pathology datasets. Some advanced methods combine multiple imaging modalities or fuse different types of data. For example, integrating H&E staining results with multiphoton microscopy images has led to more accurate diagnoses of certain conditions like microinvasion in ductal carcinoma [12]. As deep learning technology continues to advance, it has the potential to become an invaluable tool in supporting pathologists and improving the accuracy and efficiency of pathological diagnoses. There are some relevant studies on the diagnostic classification of pathological images using computer vision techniques. CNNs have shown remarkable success in pathological image analysis. A study trained CNNs on WSIs of gastric biopsy histopathology, achieving state-of-the-art results in tumor classification and segmentation [9]. Another study used CNNs to classify medical images and detect cancerous tissue in histopathological images from tissue microarrays (TMAs) [13].