The Global Cancer Statistics 2020 reported breast cancer (BC) as the most common diagnosis of cancer type. Therefore, early detection of such type of cancer would reduce the risk of death from it. Breast imaging techniques are one of the most frequently used techniques to detect the position of cancerous cells or suspicious lesions. Computer-aided diagnosis (CAD) is a particular generation of computer systems that assist experts in detecting medical image abnormalities. In the last decades, CAD has applied deep learning (DL) and machine learning approaches to perform complex medical tasks in the computer vision area and improve the ability to make decisions for doctors and radiologists. The most popular and widely used technique of image processing in CAD systems is segmentation which consists of extracting the region of interest (ROI) through various techniques. This research provides a detailed description of the main categories of segmentation procedures which are classified into three classes: supervised, unsupervised, and DL. The main aim of this work is to provide an overview of each of these techniques and discuss their pros and cons. This will help researchers better understand these techniques and assist them in choosing the appropriate method for a given use case.
Metadata
Item Type:
Article (Published)
Refereed:
Yes
Uncontrolled Keywords:
Breast cancer; Deep learning; Image segmentation; Tumor segmentation
Science Foundation Ireland under Grant number 18/CRT/6183, Science Foundation Ireland under Grant number 13/RC/2106/_P2, Science Foundation Ireland under Grant number 13/RC/2094/_P2
ID Code:
28044
Deposited On:
23 Jan 2023 13:24 by
Ramin Ranjbarzadeh Kondrood
. Last Modified 23 Jan 2023 13:24
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