Improving Ovarian Cancer Diagnosis with Deep Learning by Identification of Subtype Using VGG19 CNN Pre-Trained Model

The construction of a strong machine learning model for accurate categorization of different ovarian cancer subtypes is the main goal, approaches, results, and implications of this research work, which are summarized in the abstract. The main objective was to precisely detect subtypes including endometrioid cancer (EC), clear cell carcinoma (CC), mucinous carcinoma (MC), high-grade serous carcinoma (HGSC), and low-grade serous carcinoma (LGSC) by using the deep convolutional neural network architecture of the VGG19. Using cutting edge feature extraction methods and image processing algorithms, the model was trained on a dataset of histology photos of samples of ovarian tissues. The 75% total accuracy obtained indicates how well the suggested approach works for subtype identification. The model's classification abilities over a wide range of subtypes were evaluated thoroughly, including accuracy, recall, and F1-score. The paper not only summarizes the model's accomplishments but also clarifies possible future directions for research, as well as its advantages and disadvantages. By proving the capacity to distinguish between subgroups of ovarian cancer, the study makes a substantial contribution to the field and provides insightful information that may improve diagnostic accuracy and, in turn, the development of customized treatment plans. The growing tendency in using computational methods, especially machine learning, for improved accuracy in the detection and treatment of ovarian cancer is highlighted in the abstract's conclusion. All things considered, the abstract calls readers to explore the specifics of the study by providing a succinct and educational summary of it.