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.
Ultrasound imaging (US) is one of the most common diagnostic imaging tools for producing images of the human body in clinical practice. This work is devoted to studying ultrasound images collected from gynaecological tests for medical purposes regarding ovarian and breast defects. The study revolves around (i) Enhancing the texture of the image by applying a new effective framework that can help in reducing the speckle noise from the image while preserving the most important information; (ii) Extracting the most prominent features using the histogram of oriented gradients (HOG) and; (iii) Fusing the features that are produced by the edge operators and using them as an input to the ANN classifier to generate three trained classifiers. The fusion technique has been used to get an effective decision by using the whole features. The experimental results of the proposed method for the breast cancer and ovarian tumour using the second experiment achieved 97.96% accuracy, 96.05% sensitivity, and 99.17% specificity by utilizing the breast cancer information set. Overall, 95.87% precision, 97.01% sensitivity, and 93.33% specificity have been achieved for the ovarian tumour data collection. Consequently, the proposed method has been improved to validate the output of modern computerized and automated technologies. This method analyzes the gynaecological ultrasound images to identify suspicious objects or cases with health consequences for women.
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