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

: One of the most complex tasks for computer-aided diagnosis (Intel-ligent 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. This study developed a Viola–Jones model to enable the segmentation of breast and ovarian cancer ultrasound images. The results showed that the new approach was able to achieve signifi-cant improvements in terms of tumor segmentation for benign and malignant cases, with varying shapes and sizes of tumor. In different types of image analysis procedures, one of the most important steps involves the accuracy and automation of tumor segmentation. This is particularly true for the detection and diagnosis of ovarian and breast cancers. The hope is that the application of the proposed approach will result in more focus on the aforementioned areas. In particular, the objective is to develop and evaluate the efficiency of a new automated and computerized approach that is capable of analyzing gynaecological ultrasound images to detect and classify abnormal cases or objects that have an impact on the health of women. The computational examinations conducted in this study are motivated by the technological advancements in imaging technologies, advent of state-of-the-art models of data mining and machine learning, and image processing and analysis theories. The segmentation result of the proposed system with other existing techniques in breast cancer data set was 78.8%, while that 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 we used the breast cancer images. For the ovarian tumor, we achieved 94.84% accuracy, 96.96% sensitivity, and 90.32% specificity. For future studies, the data set used should be increased with additional difficult cases, particularly in ovarian tumor cases using deep learning approaches.