Comparative Analysis of Image Based Uterus Fibroid Detection and Classification Using Deep Learning

Fibroids are abnormal muscular tissue growing in and on the uterine wall. The fibroids are almost benign, but they can cause severe problems like abruption of the placenta, restriction of fetal growth, and repeated miscarriages. Fibroids ultimately grow in size and number if they are not detected at an early stage. Finding fibroid at the beginning is crucial so that appropriate treatment can be provided. In spite of a numerous big attempts and encouraging results in this area, the difficulty of precise segmentation and classification still exists. Fibroid detection is challenging due to differences in fibroid location, number, shape and dimension. This survey analyzed the enhancement methods, segmentation, feature extraction, classification, and deep learning approaches used for uterus fibroid analysis. Additionally, this review provides an overview of all relevant literature regarding fibroid detection, its advantages, constraints, advancements, and subsequent prospects.