## 4 Experiment Results
### Model Accuracy Performance by Pharmaceutical Type  
To evaluate how different models classify specific pharmaceutical compounds, a heatmap analysis was performed. Specifically, ResNet101 and ResNet152 demonstrated consistently high classification accuracy (above 0.90) across most medication types, including those with faint imprints or minimal color contrast. This robustness is attributed to the residual learning mechanism, which facilitates the effective propagation of both low-level visual details and high-level semantic representations. In contrast, the EfficientNet models exhibit fluctuating accuracy patterns across classes. While certain medication types are accurately classified, others--particularly those with subtle morphological differences--show sharp performance degradation. This inconsistency likely stems from the MBConv-based compression mechanism, which reduces feature resolution and limits the model's ability to preserve fine-grained visual cues essential in pharmaceutical classification. Lightweight models such as MobileNet and SqueezeNet further accentuate this trend, showing significant misclassifications across visually complex categories. Their parameter-efficient designs, while beneficial for resource-limited environments, are less suited for tasks requiring high expressive capacity. These heatmap-based visualizations play a critical role in enhancing model transparency and explainability. By offering a qualitative and class-specific perspective, they complement quantitative accuracy metrics and support a more granular evaluation of model behavior. Thus, the use of class-wise heatmaps contributes not only to performance reporting but also to the validation of architectural suitability in real-world domain-specific applications, such as pharmaceutical image classification. Figure 14 illustrates classification accuracy across different medication categories. The results indicate:  
* ResNet101 and ResNet152 maintain consistently high classification accuracy across multiple pharmaceutical categories. * MobileNet and SqueezeNet struggle with complex pharmaceutical features, demonstrating reduced accuracy in tablets with faint imprints or similar color patterns. * EfficientNet models exhibit variable performance, excelling in some medication types but performing poorly in others, suggesting potential bias toward specific feature distributions. Figure 13: Accuracy comparison of ResNet and DenseNet models. ResNet101 outperforms ResNet50 and ResNet152, while DenseNet121 achieves the highest accuracy among DenseNet variants. Sky blue was used to indicate the ResNet, and yellow for DenseNet. These results confirm that deep architectures with strong feature extraction capabilities (e.g., ResNet) are better suited for pharmaceutical classification than lightweight models optimized for mobile efficiency. In our analysis, ResNet101 and ResNet152 demonstrated consistent classification performance across the board, maintaining high accuracy (above 0.90) for most pharmaceutical classes. Their performance remained stable even for tablets with faint identifying characters or similar colors and shapes. This stability is attributed to the residual learning-based structure's ability to effectively learn and propagate both low-level visual features and high-level semantic information simultaneously. The EfficientNet family achieved high accuracy in certain classes but exhibited a marked performance degradation on visually complex or similar classes. This finding suggests that EfficientNet's MBConv-based structure limits feature resolution, potentially leading to inconsistent performance in tasks where subtle visual differences are critical, such as drug identification. MobileNet and SqueezeNet exhibited lower overall accuracy, with significant misclassifications, particularly for visually complex tablets. This result is attributed to the structural limitations of these models, which, while parameter-efficient, are limited in representational capacity.