Revolutionizing Ovarian Cancer Diagnosis: A ResNet50 Approach for Precision Classification

The abstract functions as a short explanation of the study's goals, procedures, findings, and conclusions. We intended to create a robust machine learning model that could accurately identify different subtypes of ovarian cancer, like endometrioid carcinoma (EC), clear cell carcinoma (CC), mucinous carcinoma (MC), high-grade serous carcinoma (HGSC), and low-grade serous carcinoma (LGSC). In this model, powerful image processing algorithms and feature extraction methodologies are applied to identify certain key morphological and textural traits indicative of every subtype and leverage a collection of histology images of ovarian tissue samples. The generated model achieves an overall accuracy of 75%, which indicates the utility of the provided strategy. To establish the model's classification skills across numerous subtypes, we carried out a full evaluation of its performance, including measurements of accuracy, recall, and F1-score. This model indicates possible areas for growth, future research prospects, and the model's strengths or demerits. In summary, this work demonstrates the ability to identify ovarian cancer subgroups. It delivers insightful information that could boost the accuracy or precision of diagnosis to drive the formulation of tailored treatment regimens. The literature offers effective diagnoses on the utilization of computational approaches for increased accuracy, and the treatment continues to rise.