Ovarian Cancer Detection: A Systematic Review of Deep Learning Models

A health issue that is major worldwide, with the highest death rates along with limited detection and diagnostic methods is nothing but ovarian cancer. The earliest detection and the precise diagnosis of ovarian cancer have a great impact on improving the outcome of the patient from this deadliest disease by offering significant treatment plans. Various medical imaging techniques play a vital role in the diagnosis of ovarian cancer. Some of the widely used datasets, pre-processing steps, and segmentation techniques that are involved in the detection of ovarian cancer offer crucial results for the enhancement of the diagnosis process. As the technology reached its peak, the employment of the Artificial Intelligent (AI) techniques has resulted in achieving promising outputs. Out of this, deep learning architectures are playing a prominent role in increasing the diagnostic accuracy of ovarian cancer. This paper serves as a systematic review of the widely used deep learning classification models that are employed in the detection and diagnosis of ovarian cancer with effective robustness. The study also summarizes by proving that deep learning techniques applied for the diagnosis of ovarian cancer using the medical imaging modalities and the other predefined processes used before the classification step namely dataset description, pre-processing, and segmentation have increased the patient's outcome from this deadliest disease, by offering the enhanced treatment plans. The choice of filtering techniques, segmentation and classifier models play a crucial role in improving the performance of the model. Because the images present in dataset may not be in acceptable form. Hence, the choice of aforementioned techniques takes part in improving the prediction as well as facilitates the process of diagnosis at early stage. Additionally, with the help of the evaluation metrics namely accuracy, precision, recall, and F1-score the performance of the classification models used is ex...