Open Access

ARTICLE

Fully Automatic Segmentation of Gynaecological Abnormality Using a New Viola–Jones Model

Ihsan Jasim Hussein¹, M. A. Burhanuddin², Mazin Abed Mohammed^3,*, Mohamed Elhoseny⁴, Begonya Garcia-Zapirain⁵, Marwah Suliman Maashi⁶, Mashael S. Maashi⁷

1 BIOCORE Research Group, Universiti Teknikal Malaysia Melaka, Melaka, 76100, Malaysia
2 Director of UTeM International Centre, BIOCORE Research Group, Universiti Teknikal Malaysia Melaka, Melaka, 76100, Malaysia
3 College of Computer Science and Information Technology, University of Anbar, Ramadi, 31001, Iraq
4 Department of Computer Science, College of Computer Information Technology, American University in the Emirates, 503000, United Arab Emirates
5 eVIDA Laboratory, University of Deusto, Bilbao, 48007, Spain
6 Medical Laboratory Science Department, King Abdulaziz University, Jeddah, 21589, Saudi Arabia
7 Software Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh, 11451, Saudi Arabia

* Corresponding Author: Mazin Abed Mohammed. Email:

(This article belongs to this Special Issue: Intelligent Decision Support Systems for Complex Healthcare Applications)

Computers, Materials & Continua 2021, 66(3), 3161-3182. https://doi.org/10.32604/cmc.2021.012691

Received 09 July 2020; Accepted 13 October 2020; Issue published 28 December 2020

Abstract

One of the most complex tasks for computer-aided diagnosis (Intelligent decision support system) is the segmentation of lesions. Thus, this study proposes a new fully automated method for the segmentation of ovarian and breast ultrasound images. The main contributions of this research is the development of a novel Viola–James model capable of segmenting the ultrasound images of breast and ovarian cancer cases. In addition, proposed an approach that can efficiently generate region-of-interest (ROI) and new features that can be used in characterizing lesion boundaries. This study uses two databases in training and testing the proposed segmentation approach. The breast cancer database contains 250 images, while that of the ovarian tumor has 100 images obtained from several hospitals in Iraq. Results of the experiments showed that the proposed approach demonstrates better performance compared with those of other segmentation methods used for segmenting breast and ovarian ultrasound images. The segmentation result of the proposed system compared with the other existing techniques in the breast cancer data set was 78.8%. By contrast, the segmentation result of the proposed system in the ovarian tumor data set was 79.2%. In the classification results, we achieved 95.43% accuracy, 92.20% sensitivity, and 97.5% specificity when we used the breast cancer data set. For the ovarian tumor data set, we achieved 94.84% accuracy, 96.96% sensitivity, and 90.32% specificity.

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Citations