## 3 Materials and Methods
### Classification Model
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Web-Based Model** & **Pathology** & **Accuracy** & **F1 Score** \\ \hline \multirow{4}{*}{Azure Machine Learning} & Melanoma & 0.90 & 0.90 \\  & Actinic keratosis & 0.93 & 0.93 \\  & Seborrheic keratosis & 0.95 & 0.95 \\  & Basal cell carcinoma & 0.92 & 0.92 \\ \hline \multirow{4}{*}{Google Teachable} & Melanoma & 0.94 & 0.94 \\  & Actinic keratosis & 0.94 & 0.94 \\  & Seborrheic keratosis & 0.98 & 0.98 \\  & Basal cell carcinoma & 0.98 & 0.98 \\ \hline \multirow{4}{*}{Google Cloud—Vision AI} & Melanoma & 0.97 & 0.97 \\  & Actinic keratosis & 0.95 & 0.95 \\  & Seborrheic keratosis & 0.97 & 0.97 \\  & Basal cell carcinoma & 0.96 & 0.96 \\ \hline \multirow{4}{*}{Einstein Vision} & Melanoma & 0.91 & 0.91 \\  & Actinic keratosis & 0.91 & 0.91 \\  & Seborrheic keratosis & 0.91 & 0.91 \\  & Basal cell carcinoma & 0.90 & 0.90 \\ \hline \multirow{4}{*}{Azure Machine Learning} & Glaucoma & 0.96 & 0.96 \\  & Cataract & 0.90 & 0.90 \\  & Diabetic retinopathy & 0.96 & 0.96 \\ \hline \multirow{4}{*}{Google Teachable} & Glaucoma & 0.97 & 0.97 \\  & Cataract & 0.96 & 0.96 \\  & Diabetic retinopathy & 0.94 & 0.94 \\ \hline \multirow{4}{*}{Google Cloud—Vision AI} & Glaucoma & 0.98 & 0.98 \\  & Cataract & 0.98 & 0.98 \\  & Diabetic retinopathy & 0.98 & 0.98 \\ \hline \multirow{4}{*}{Einstein Vision} & Glaucoma & 0.91 & 0.91 \\  & Cataract & 0.87 & 0.87 \\ \cline{1-1}  & Diabetic retinopathy & 0.90 & 0.90 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Experimental results for web-based models on various pathology. GT is often used for educational purposes, enabling students and enthusiasts to experiment with machine learning concepts in a hands-on way. It has applications in a wide range of fields, including education, art, design, and prototyping, allowing users to explore the possibilities of machine learning in creative ways. At its core, a pre-trained Convolutional Neural Network (CNN) forms the basis, with user-specific classes integrated as the final layer. MobileNet serves as the designated model for image classification, and the resulting models can seamlessly integrate into JavaScript-based applications. GT offers three distinct model types for various classifications: MobileNet for image classification, Speech Commands for audio classification, and PoseNet for real-time body position classification in video images [41]. In the realm of pathology classification, the utilization of CNNs offers notable advantages. Firstly, CNNs are adept at hierarchical feature extraction, autonomously acquiring hierarchical representations of features from raw input data. Specifically in the domain of eye-related issues, CNNs demonstrate proficiency in discerning intricate patterns and structures from medical images such as fundus images or retinal scans, enabling them to capture subtle details and variations associated with different eye conditions. Secondly, CNNs excel in capturing spatial relationships within images, a critical aspect in identifying abnormalities or anomalies in the eye's structure within eye-related conditions. Leveraging this capability, CNNs can discern spatial patterns indicative of various conditions, including optic nerve abnormalities, retinal lesions, or vascular changes. Moreover, CNNs leverage local receptive fields, enabling them to focus on small image regions and extract features specific to those areas. This capability may be particularly advantageous in the analysis of medical images, where localized abnormalities may serve as indicators of particular conditions. By examining local features, CNNs provide insights into the spatial distribution and extent of abnormalities within the eye. Additionally, CNNs can harness transfer learning, a technique where knowledge acquired from pre-trained models on extensive datasets can be adapted and fine-tuned for specific tasks. Pre-trained CNN models trained on diverse datasets of natural images capture general visual representations, which can be effectively applied to the analysis of medical images.