Fully Automatic Segmentation of Gynaecological Abnormality Using a New Viola–Jones Model
Hussein, Ihsan Jasim . 2021
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.
