# The Diagnostic Classification of the Pathological Image Using Computer Vision
## 2 Applications of Deep Learning Approaches for Diagnostic Classification  
Deep learning approaches have shown significant promise for the diagnostic classification of various diseases. Deep learning has been applied to diagnose and classify a wide range of diseases, including heart disease, diabetes, cancer, Alzheimer's disease, liver disease, pneumonia, skin diseases, genetic disorders, and neurological conditions [17; 18; 19; 20; 21; 22; 23; 24]. Some of the most frequently used deep learning algorithms for disease diagnosis include CNNs, recurrent neural networks (RNNs), artificial neural networks (ANNs), and Support Vector Machines (SVMs) [25]. Among them, CNNs are particularly effective for analyzing medical imaging data like X-rays, Computed Tomography (CT) scans, and MRIs [26; 27; 28]. They can automatically extract relevant features from images to classify diseases [29]. RNNs are useful for analyzing sequential data like time series of vital signs or electronic health records [30]. They can capture temporal dependencies in the data. As for ANNs, standard feedforward neural networks have been applied to structured clinical data for disease prediction and classification [31]. While not strictly deep learning, SVMs are still commonly used and compared against deep learning approaches. Deep learning algorithms have demonstrated impressive accuracy in diagnosing various diseases, often matching or exceeding the performance of human healthcare professionals [32]. For example, a study proposed using DenseNet-169 and ResNet-50 CNN architectures for diagnosing and classifying [PERSON]'s disease. This approach achieved high accuracy, with some models reaching up to 98% accuracy in classification tasks [33]. Another research investigated the use of VUNO Med-DeepBrain AD, a deep learning algorithm, as a decision support tool for Alzheimer's diagnosis. This model showed an accuracy of 87.1%, which was slightly higher than the 84.3% accuracy achieved by medical experts using traditional diagnostic methods [34]. A systematic review highlighted that deep learning methods, such as CNNs and recurrent neural networks (RNNs), have been used extensively for Alzheimer's disease classification. These methods have achieved accuracies of up to 96% for Alzheimer's classification and 84.2% for predicting the conversion from mild cognitive impairment (MCI) to Alzheimer's [35]. Despite the promising results, there are challenges associated with using deep learning for Alzheimer's diagnosis. The performance of deep learning models can be affected by the quality and variability of neuroimaging data used for training [36]. Understanding how these models make decisions is crucial for clinical adoption [36]. Ensuring that models generalize well across different populations and imaging protocols is essential for widespread clinical use [37; 38]. While deep learning models show significant promise in improving the accuracy and speed of Alzheimer's disease diagnosis, ongoing research is needed to address these challenges and enhance their clinical applicability. In addition, deep learning models have shown high accuracy in detecting cancers like gastrointestinal and liver cancer, at early stages. A study developed a deep learning system using CNNs to classify liver tumors based on MRI. This system achieved performance comparable to experienced radiologists in classifying liver tumors into seven categories, with an AUC of 0.985 for hepatocellular carcinoma and 0.998 for metastatic tumors [39]. The use of unenhanced images combined with clinical data improved the classification accuracy, potentially reducing the need for contrast agents. Another approach used an improved U-Net model for liver tumor segmentation from CT scan images, achieving a dice score of 0.96 for liver subdivision and 0.74 for tumor segmentation [36]. This method enhanced efficiency in diagnosing liver tumors by leveraging 3D spatial features and temporal domain information.