## 4 Experiment Results
### Comparative Model Accuracy Performance Analysis  
To assess the classification accuracy of the proposed ResNet101-based model, we conducted a comparative analysis with other CNN architectures, including DenseNet, EfficientNet, MobileNet, SqueezeNet, and VGGNet. The accuracy values were computed with high precision, rounded to the fourth decimal place. Given that precise medication classification is critical for preventing pharmaceutical misuse, our evaluation prioritized models that could avoid ambiguous classifications where a single tablet might be categorized under multiple medication types. Therefore, classification accuracy was used as the principal performance metric. Each deep learning model was trained and fine-tuned using pre-trained weights from the pharmaceutical image dataset. The evaluation process ensured consistent data partitioning and hyperparameter tuning across all models. Figure 12 illustrates the classification accuracy of the evaluated CNN architectures. ResNet101 achieved the highest accuracy (98.51%), followed by ResNet152 (95.94%) and EfficientNet B6 (95.40%). In contrast, SqueezeNet_0 (0.25%) and MobileNet V3 Small (1.37%) demonstrated significantly lower performance. ResNet101's superior accuracy can be attributed to its residual learning architecture, which effectively prevents gradient vanishing and facilitates stable gradient propagation through skip connections. This structure allows the model to retain and transmit both low-level and high-level visual features, thereby enabling fine-grained recognition of subtle tablet characteristics--such as engravings, imprints, and minor color variations--that are critical for accurately distinguishing visually similar pharmaceutical samples. Figure 11: Mobile interface of the pharmaceutical identification system. The result screen displays the classified medication name and dosage information. In contrast, EfficientNet's compound scaling approach prioritizes computational efficiency using MBConv blocks and parameter reduction. While this design is effective for general-purpose tasks (e.g., ImageNet classification), it may limit feature resolution and expressive capacity in domain-specific applications such as pharmaceutical classification, where fine-grained local features are essential. DenseNet architectures demonstrated notable accuracy, with DenseNet121 (81.75%) and DenseNet201 (76.92%) benefiting from dense connectivity and feature reuse, which enhanced gradient propagation and facilitated the learning of tablet-specific features. Conversely, lightweight models such as SqueezeNet, MobileNet, and VGGNet exhibited lower accuracy due to their simplified architectures and compression-driven constraints, which limited representational depth and capacity. Conversely, SqueezeNet, MobileNet, and VGG models recorded relatively lower accuracy due to their design priorities:  
* SqueezeNet and MobileNet focus on model compression and parameter reduction, limiting their feature extraction capacity. * VGGNet employs a simplistic architecture that lacks the deep-layer optimizations of modern CNNs. * EfficientNet, while optimized for compound scaling, offers limited depth and feature resolution, making it less suitable for complex pharmaceutical classification tasks. To further analyze performance trends, Figure 13 provides a breakdown of ResNet and DenseNet variants, demonstrating how architectural modifications impact accuracy. Despite the general assumption that deeper networks yield better accuracy, our findings reveal a diminishing return effect in certain cases:  
* ResNet101 (98.51%) outperformed ResNet152 (95.94%), highlighting that network depth alone does not guarantee improved accuracy. * DenseNet121 (81.75%) outperformed DenseNet201 (76.92%), suggesting that deeper DenseNet models may experience overfitting in pharmaceutical image classification. These findings emphasize that model efficiency and architectural balance play a more crucial role than raw parameter count in pharmaceutical classification tasks.