## 4 Discussion  
This study aimed to identify the damage caused by cisplatin, a chemotherapeutic agent administered to cancer patients, on female ovarian tissue and to analyze the restorative effects of exosomes derived from mesenchymal stem cells. The classification results for Cis, Exo, Control, CisExo, and ExoCis groups were evaluated. Images from the female reproductive system, particularly follicular images containing oocytes, obtained from the pathology laboratory were included in the analysis. To define the region's boundaries in the images, a model based on artificial intelligence was used to select four reference points. Oocyte-containing follicles were cropped from the images within the specified coordinates, generating new images for detailed analysis. A total of 177 features were extracted per image, including 59 LBP features from each RGB channel. A k-NN classifier was used [32]. Specific points around the follicle (brown, white, and purple regions) were labeled. This trained model was then applied to all images to generate enhanced, trained versions of the images. To identify the most significant features, Lasso regression was applied with an experimentally determined lambda value [36]. The selected unique features for each group were classified using ANN, KNN, SVM, and DT models. The selected distinctive features enhanced classification precision. Due to the restorative effects of the Exosome group, experts hypothesized its similarity to the Control group. Upon examining the accuracy values of classification models for the Control-Exo group in Table 2, DT achieved 67% \(\pm\) 0.04, KNN achieved 70% \(\pm\) 0.04, SVM achieved 59% \(\pm\) 0.07, and ANN achieved 68% \(\pm\) 0.05. The classification performance of the models was lower for the Control-Exo group compared to the Control-Cisplatin group, likely due to the similarity of features selected for the Control and Exo groups. Furthermore, the violin plot shown in Figure 6 confirms that the Control group and the Exo group are similar in terms of features. For the Control-Cisplatin group, a high classification performance was anticipated due to the selection of distinctive features. As shown in Table 1, the results for KNN, SVM, and ANN were 92% \(\pm\) 0.02, 90% \(\pm\) 0.02, and 92% \(\pm\) 0.05 accuracy, respectively. The significant differences in the features between the Control and Cisplatin groups enabled the classification models to distinguish these groups with approximately 91.33% accuracy. The classification results for the Control and Exo-Cis groups (Table 4) revealed an accuracy of 61% \(\pm\) 0.02 for the DT model, while KNN, ANN, and SVM achieved approximately 74.66% accuracy. Despite expectations that the Control-ExoCis group would yield results similar to the Control-Cis group, it was observed that administering exosomes before cisplatin had a modest restorative effect, reducing the impact of cisplatin compared to the Control-Cis group. Analysis of the Control and Cis-Exo groups (Table 3) revealed that the exosome administered after cisplatin was expected to have a restorative effect, leading to similarities between the Control group and Cis-Exo group's distinctive features. Contrary to expectations, DT achieved 76% \(\pm\) 0.12 accuracy, whereas the other models yielded approximately 87% accuracy. These findings suggest that exosomes administered after cisplatin did not effectively restore the damage caused by cisplatin. In the classification of Control and Cis groups, the KNN algorithm outperformed others with an accuracy of 92% and an F-score of 92%. Similarly, ANN and SVM algorithms demonstrated strong performances, achieving over 90% accuracy. For the Exo and Control groups, ANN provided balanced results with an accuracy of 68% and an F-score of 60%. Particularly in the CisExo group, the high accuracy and F-score values obtained by ANN and KNN algorithms indicated that the features of this group were more effectively learned.