## 4 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Reference** & **Year** & **CNN Model** & **No. of Images** & **Accuracy** & **F1 Score** \\ \hline [PERSON] et al. [46] & 2024 & CNN+SVM & 6140 & 0.9200 & - \\ \hline [PERSON] [45] & 2024 & MobileNetV3 & 14,000\textgreater{} & - & 0.9600 \\ \hline [PERSON] et al. [43] & 2024 & LeNet-5 & 84,484 & 0.9600 & 0.9600 \\ \hline [PERSON] et al. [48] & 2024 & VGG16 & 5228 & - & - \\ \hline \multirow{3}{*}{[PERSON] and [PERSON] [24]} & \multirow{3}{*}{2024} & Ensemble & \multirow{3}{*}{5856} & \multirow{3}{*}{0.9503} & \multirow{3}{*}{0.9604} \\  & & (InceptionResNetV2, & DenseNet201, VGG16) & & \\ \hline \multirow{3}{*}{[PERSON] et al. [44]} & \multirow{3}{*}{2022} & Ensemble (MobileNetV2, & \multirow{3}{*}{5856} & \multirow{3}{*}{0.9391} & \multirow{3}{*}{0.9343} \\  & & DenseNet169, Vision & & & \\ \cline{1-1}  & & Transformer) & & & \\ \hline [PERSON] et al. [22] & 2019 & Custom CNN & 5856 & 0.9531 & - \\ \hline [PERSON] et al. [49] & 2024 & DenseNet121 & 578 & 0.8100 & - \\ \hline [PERSON] [28] & 2024 & DenseNet121 & 5856 & 0.9503 & 0.9603 \\ \hline [PERSON] et al. [47] & 2021 & ResNet50 & 5232 & 0.9306 & 0.9271 \\ \hline [PERSON] et al. [29] & 2022 & Custom CNN & 5857 & 0.9280 & 0.9430 \\ \hline [PERSON] and [PERSON] [42] & 2022 & SGDRE method & 5858 & 0.9626 & 0.9744 \\ \hline [PERSON] et al. [50] & 2023 & Custom CNN & 5856 & 0.9609 & - \\ \hline
**Our approach** & & & **5856** & **0.9642** & **0.9640** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison and evaluation of the proposed approach and scientific studies in the last six years according to the data from the Web of Science Core Collection. achieve competitive F1 scores of 0.9604 and 0.9343, respectively. Despite these advantages, the complexity and resource demands of ensemble architectures pose significant challenges in terms of interpretability, computational efficiency, and scalability. Custom CNNs, including those developed by [PERSON] et al. [22], [PERSON] et al. [29], and [PERSON] et al. [50], exhibit flexibility and adaptability to specific datasets, yielding accuracy values ranging from 0.9280 to 0.9609. Their performance highlights the potential of tailored architectures; however, the omission of F1 scores in some cases and slightly lower performance compared to pre-trained models suggest limited generalizability and challenges in capturing complex data patterns. Pre-trained architectures like DenseNet and ResNet demonstrate consistent performance due to their robust design. For example, [PERSON] [28] and [PERSON] et al. [47] reported accuracy values of 0.9503 and 0.9306, respectively, showcasing the efficacy of DenseNet121 and ResNet50 in feature propagation and handling gradient-related issues. However, DenseNet's performance can vary depending on dataset size and complexity, as evidenced by the low accuracy of 0.8100 achieved by [PERSON] et al. [49] on a small dataset of 578 images. Among lightweight models, [PERSON] [45] utilized MobileNetV3 to achieve an F1 score of 0.9600 on a larger dataset (>14,000 images), demonstrating its efficiency for resource-constrained environments. However, the absence of accuracy metrics limits a holistic evaluation of its performance. Similarly, foundational architectures like LeNet-5, used by [PERSON] et al. [43], achieved competitive accuracy and F1 scores of 0.9600 on a significantly larger dataset with 84484 images, though their simplicity may constrain their ability to capture complex features compared to modern architectures. Optimization-based approaches such as the SGDRE method by [PERSON] and [PERSON] [42] delivered an outstanding performance, achieving the highest F1 score of 0.9744 and an accuracy of 0.9626. While this underscores the potential of targeted optimization strategies, such methods might face limitations in adaptability across diverse datasets.