## 4 Discussion
According to the boxplot results in Figure 1, DT and ANN models displayed narrower accuracy ranges and lower standard deviations, reflecting more consistent and stable performance. These models achieved similar accuracy levels across different groups, demonstrating less variability. On the other hand, KNN and SVM models exhibited broader accuracy ranges and higher standard deviations, indicating more variable performance. SVM, in particular, showed low accuracy in some groups, resulting in more pronounced fluctuations and higher standard deviation. Overall DT and ANN provided more consistent results, while KNN and SVM showed greater variability. The analyses suggest that exosome administration may be an effective method to mitigate the side effects of cisplatin on ovarian tissue in female cancer patients. Additionally, this study highlights the potential of exosome-based therapeutic approaches to reduce chemotherapy side effects. The high homogeneity and classification success observed in the CisExo group support the notion that exosome application can alleviate cisplatin-induced damage. This finding is significant for developing strategies to minimize cisplatin's side effects and preserve reproductive health post-cancer treatment. The analysis of LBP features was conducted using statistical methods, resulting in a total of 177 features. Since none of the features followed a normal distribution according to the normality test, the Kruskal-Wallis test [57; 58] was applied. For features that showed significant differences, a post hoc Dunn test [57] was performed, identifying 51 features with significant differences. Notably, significant differences were observed between the CisExo-Cis and CisExo-Exo groups. When visualized based on p-values [57] for the significant features, feature 114 demonstrated better performance compared to the others. To further evaluate classification performance and provide a comparative perspective, we also applied a deep learning-based approach using AlexNet. Below, we summarize its methodology and findings. A five-fold cross-validation strategy was applied, with training and test datasets randomly split for each fold. Transfer learning was employed by fine-tuning AlexNet's pre-trained network, where the final layers were modified to match the dataset. The training process used the Adam optimizer with a learning rate of 0.0001, a mini-batch size of 16, and 12 epochs. Classification performance was evaluated using precision, recall, specificity, accuracy, and F1-score metrics. The results revealed that the highest classification accuracy was achieved in the Control vs. Cisplatin group, consistent with the distinct textural alterations caused by cisplatin. The lowest accuracy was observed in the Control vs. Exosome group, supporting the finding that exosome-treated tissues closely resemble the Control group in texture. Furthermore, the Exosome + Cis group showed lower classification accuracy compared to Cis + Exosome, reinforcing the significance of treatment sequence on tissue recovery. While AlexNet-based deep learning provided robust classification performance, its inability to elucidate specific histopathological feature changes limits its interpretability. Therefore, LBP-based feature extraction and machine learning approaches remain more suitable for understanding the mechanistic effects of cisplatin and exosome treatments on ovarian tissue structure. For the first time in the literature, this study analyzed the effects of cisplatin and exosomes on female ovarian tissues using histopathological images with LBP-based feature extraction and machine learning classification methods. The multiclass framework, including different combinations of cisplatin and exosome treatments, provided a detailed examination of pre- and post-treatment conditions. This study introduces a novel approach for clinical and biomedical research. By comparing the performance of various machine learning algorithms, the effects of cisplatin and exosomes were analyzed more precisely.