• 2022-05
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  • 2021-03
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  • 2019-11
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  • br quite robust br Figure Comparison of


    quite robust.
    Figure 6. Comparison of two ROC curves generated using (a) training dataset and (b)
    testing dataset.
    After applying an operating threshold (T = 0.5) to divide the test cases into two predicted ZD 1839 being malignant or benign, we generated two confusion matrices of testing results, which are presented at Table 4. Based on the two confusion matrices, we further computed other assessment indices such as classification sensitivity, specificity, positive and negative predictive values, and odds ratio, which were summarized in Table 5. The results show that the SVM classifier optimized with features computed from two-view images yielded better classification performance than the classifier optimized with four-view image features.
    Table 4. Two confusion matrices of applying two SVMs to classify between malignant and benign cases.
    Two-view images Four-view images Actual Malignant Benign Malignant Benign Prediction
    Table 5. Summary of other assessment indices of two SVM classifiers optimized using two-view and four-view images.
    4. Discussion
    Although several imaging modalities have been tested and/or applied as breast cancer screening tools [27], mammography remains the most cost-effective and widely used tool for the population based breast cancer screening. However, a large amount of suspicious breast lesions (particularly the soft tissue masses) can be detected in mammograms and the majority of them are benign. Thus, exploring new approach to develop more effective CAD schemes to assist the classification between malignant and benign cases or lesions depicting on mammograms is crucial to improve the efficacy of the breast cancer screening and diagnosis [7]. In this study, we developed a novel CAD scheme utilizing the global mammographic image features to predict likelihood of the testing cases being malignant without lesion segmentation. As compared to the previously reported research efforts, our investigation has a number of unique characteristics or new observations as follows:
    First, instead of computing image features from the segmented lesion and its surrounding area, the new CAD scheme extracts and computes the global image features from the whole breast areas of mammograms in this study. The majority of previous studies of CAD-based breast lesion classification [28] computed image features from the segmented lesions or its neighbors, which may have advantages and disadvantages. The advantages include enabling to compute features that are more focused and/or relevant to the specific lesions, while the disadvantages include the variable or lower accuracy and/or reproducibility of computing the features due to the difficulty and errors in lesion segmentation. In our investigation, when we use the global features extracted from the two-view positive images of one breast with suspicious lesion detected, the trained SVM classifier yielded a comparable performance (i.e. AUC 0.79±0.07) in classification between the malignant and benign cases. Although we cannot directly compare the classification performance (AUC value) of our new scheme with previously reported CAD schemes due to the use of different image datasets, we believe that our new case-based CAD scheme yielded encouraging and comparable performance as many of previously developed lesion-based schemes (i.e., AUC values ranging from 0.70 to 0.86 as presented in a review table of a previous paper [11, 23]). The new study result indicates that the clinically meaningful information is not only focused on the lesion, but also distributes on the entire breast area of mammogram image. In addition, although CAD schemes without lesion segmentation
    have been previously developed and reported in the literature (i.e., [10, 14]), these schemes computed image features from a fixed region of interest (ROI) covering the suspicious lesions, which have disadvantages or difficult to adaptively identify the optimal size of the ROIs to cover the lesions with varying size and shape. The approach in this study is different. Thus, to the best of our knowledge, this is the first study that investigate the feasibility of developing a global breast image feature-based CAD scheme to classify between malignant and benign mammographic cases, which avoid difficulty in both segmentation of the lesions and determination of the optimal ROIs, which are the two popular approached used in previous studies.
    Second, we trained and tested two SVM classifiers using two feature pools containing the global images features computed from two-view images of one positive breast and four-view images of two breasts. The testing results show that the SVM classifier yielded AUC of 0.79±0.07 when two-view images of one positive breast were involved in the training and testing process. However, when using the image features computed from four-view images of two breasts to build the SVM classifier, the scheme yielded a reduced performance with an AUC of 0.75±0.08, which implies that the discriminatory information or power may be diluted when adding two negative images of one cancer-free breast. Thus, Enelopes should be better to use two-view images of one breast to train the SVM classifier. Then, CAD scheme can be applied to two-view images of left and right breasts separately. The higher classification score should be selected to represent the likelihood of the testing case being malignant.