Comparative Assessment of deep learning methods for Prediction of Uterine Fibroid

Abstract

Today, convolutional networks, a type of deep learning algorithm, are frequently used in medical science. Automatic disease detection, in particular, is of great importance in the medical field. For the detection and classification of tumors, it is necessary to segment the affected region and the tumor. In general, medical imaging techniques are used to diagnose the tumor, such as magnetic resonance imaging (MR), ultrasound (US) and computed tomography (CT). Our study surveysdeep learning's applications for image classification, object detection, segmentation. Accurate segmentation of the affected region, tumors, and spine from MR images remains challenging, however it is still challenging to achieve because of 1) Wide differences in the size and shape of tumors amongst individuals; 2) the lack of contrast between tissues and organs that are next to each other; and 3) the undetermined number of tumors. Neural networks are able to capture the region and tumors [2]. The survey concludes with current findings and an analysis of open research challenges and directions.

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