The Effectiveness of CNN in Evaluating Ultrasound Image Datasets: Diagnosing Polycystic Ovary Syndrome (PCOS) as an Example

Abstract

Deep learning has proved its potential and the vital role it can provide in benefitting and assisting practitioners that use ultrasonography as a tool for diagnosis. However, medical datasets are often very difficult to obtain. Moreover, erroneous or fake training datasets may lead to inaccurate results. This paper is discussing the robustness of deep learning in diagnosing polycystic ovary syndrome (PCOS). Two public online data sets were used to train and test the ability of transfer learning using DenseNet201 architecture in detecting the infected ovaries. The two datasets showed different results although the same model, architecture and parameters were used. Further investigation showed that one of the data sets is extremely erroneous and of misleading information. In conclusion, Convolutional Neural Networks (CNN) and Transfer Learning can be considered as a strong tool to evaluate datasets.

Keywords

• convolutional neural networks (cnns); transfer learning; deep learning; polycystic ovary syndrome (pcos); medical ultrasound image processing; classification; misleading datasets.