## 1 Introduction
The classification and identification of ovarian masses remain challenging due to the complexity of image backgrounds [29]. This study utilized the Gray-Level Co-Occurrence Matrix (GLCM) and Tamura texture features to classify ovarian abnormalities. Various texture features such as coarseness, contrast, directionality, and roughness were extracted, and a classifier resembling Light Glioblastoma (GBM) was employed for analysis. The classification results showed that combining GLCM and Tamura texture features, even without segmentation, achieved an accuracy rate of 72%. [PERSON] et al. [30] demonstrated the importance of determining cisplatin (CP) resistance, particularly for ovarian adenocarcinoma. cinoma cells. GLCM features were extracted from images to characterize textures. Ovarian carcinoma cell lines of serous type (OVCAR-4 and A2780) and clear cell type (IGROV1) with CP resistance were used to demonstrate GLCM texture feature extraction from images. The signal-to-noise ratio was employed to evaluate the chemoresistance grade of cell images derived from GLCM texture feature extraction. The differences between wild-type and CP-resistant cells were statistically significant in all cases (\(p<0.001\)). GLCM-based in vitro and ex vivo feature extraction reliably identified CP-resistant ovarian adenocarcinoma cells. This study investigates the histopathological damage caused by cisplatin, a commonly used chemotherapeutic agent, on the female reproductive system--particularly ovarian tissues. It also evaluates the potential restorative effects of exosome application on this damage. Although cisplatin is successfully employed in the treatment of various cancer types due to its chemotherapeutic efficacy, it can cause significant side effects to female reproductive health. In this context, understanding the potential reparative effects of exosome application on cisplatin-induced damage holds critical importance for the development of treatment approaches. The effects of cisplatin and exosome applications on ovarian tissues were evaluated using advanced analytical methods. Local Binary Pattern (LBP) features were initially extracted from ovarian histopathological images. These features were then analyzed across four distinct classes: Cisplatin-Control (Cis-Control), Cisplatin+Exosome-Control (Cis+Exo-Control), Exosome+Cisplatin-Control (Exo-Cis-Control), and Exosome-Control (Exo-Control). These classes were designed to investigate the effects of cisplatin and exosome applications on ovarian tissues, and classification was performed using machine learning algorithms. The literature contains limited comprehensive studies aiming to mitigate the side effects of cisplatin on ovarian tissues through exosome applications. This study addresses this gap by integrating image processing and machine learning methods to present a novel approach. It provides a comprehensive analysis of both the damage caused by cisplatin alone and the potential reparative effects of exosomes on this damage. The highlights of the study:  
* Damage to ovarian tissues: A detailed analysis of the histopathological effects of cisplatin was conducted. * Restorative effect of exosomes: The potential therapeutic effects of exosome applications were examined. * Integration of machine learning and image processing: A novel approach was presented to classify the effects of different applications using these methods. * Comprehensive analysis: Cisplatin and exosome applications were analyzed both individually and in combination, providing new insights for treatment strategy. * Accuracy: The images are manually classified by experts based on several pathological criteria. To reduce potential errors by pathologists and expedite the result acquisition process, more precise outcomes are achieved through automated classification methods.