Advanced Ovarian Cyst Diagnosis and Classification through Deep Learning in Ultrasound Images

Ovarian cysts pose a serious health risk to women of all ages and backgrounds all around the globe. In medical diagnosis, distinction of ovaries affected by cysts from the normal ovaries through ultrasound images is a crucial task. Nowadays, deep learning models have emerged as effective tools for classification tasks in the field of medical imaging. This paper employs Residual Network (ResNet), EfficientNet, Densely connected Network (DenseNet) and Visual Geometry Group (VGG) models that have depicted impressive performance in automating the classification process. The efficacy of the models in detecting the existence of ovarian cysts is assessed by the performance standards, namely accuracy, precision, recall, specificity, f1-score and area under receiver operating curve. DenseNet-169 has proved to be the best deep learning model for precise and efficient classification of ovarian cysts with accuracy 99.78% in ultrasound images. This research contributes a major milestone in the classification of cystic ovaries from normal ovaries and henceforth, leads in the advancement of diagnostic capabilities in the field of ovarian pathology.