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# False Positive Reduction in Mammographic Mass Detection

S. Shrithi, R. Lavanya(*), and Devi Vijayan

Amrita School of Engineering, Amrita Vishwa Vidyapeetham University, Coimbatore, India

r_lavanya@cb.amrita.edu

**Abstract.** A mortal disease affecting women globally is breast cancer. The survival rate rises in the matter of breast cancer when the presence of mass is identified ahead of time through a mammogram. The mass is obscured in dense breast tissues and hence the sensitivity is limited in the case of mammography. A computer aided diagnosis (CAD) system helps in overcoming the sensitivity issue in mammography but in turn the system is prone to many false positives. The proposed work incorporates the development of automated density-specific models for false positive reduction. A feature-classifier combination that performs the false positive reduction efficiently in each expert model has been identified. Normal and abnormal mammograms of all the four density types from the Image Retrieval in Medical Applications (IRMA) version of the Digital Database for Screening Mammography (DDSM) database have been employed in this work, resulting in the classification accuracy of 96%, 80%, 76% and 88% and false positive rates (FPR) of 0, 0.26, 0.25 and 0.12 for each density-specific mass detection models respectively.

**Keywords:** Breast cancer · Breast density · False positive reduction · Mammogram · Mass · Mass detection

## 1 Introduction

Breast cancer is a mortal disease that affects women universally. An estimation states that, annually, about 508,000 female population die from breast cancer [1]. Mammography is utilized for detecting breast cancer in prior [2]. Breast Imaging Reporting and Data System (BI-RADS) has classified breast density into the following four categories such as almost entirely fatty, scattered areas of dense tissues, heterogeneously dense and extremely dense. The sensitivity of the mass detection decreases as the density increases [3, 4]. The dense areas and masses present in the breast tissue tend to appear bright in a mammogram resulting in an undifferentiable appearance. Whereas, the non-dense areas appear dark as the X-rays transverse through them [5, 6].

Computer Aided Diagnosis (CAD) systems are built for automating the mass detection in the breast tissues. A CAD system incorporates the steps starting from preprocessing followed by segmentation, extraction of features, feature selection and classification. Generally, a CAD system helps in overcoming the lower sensitivity issue
occurring in the process of mammography. The segmentation part of the CAD system tends to identify all the suspicious regions as masses increasing the sensitivity as well as the number of false positives. This condition generally occurs in the case of dense breasts where the mass as well as the dense breast tissue appears bright making it difficult to conclude the breast tissue as abnormal or normal. So, false positive reduction is performed following the mass detection stage to precisely classify the breast tissue as normal or abnormal [7].

The proposed work mainly focuses on the development of automated density-specific mass detection models that incorporates the false positive reduction stage. Four separate models have been developed for addressing each breast density category to avoid misinterpretations as it is tedious for a single model to adapt according to each breast density type and provide the result. Features extracted using various detector and descriptor combinations have been employed and a comparative analysis has been done to identify the significant feature detector and descriptor for each model. A comparative analysis of the density-specific mass detection models and a non-density specific mass detection model has also been performed to highlight the significance of density-specific models.

The paper is arranged in this manner; Sect. 2 comprising the Literature Survey, Sect. 3 dealing with the Proposed Methodology, Sect. 4 including the Results and Discussion and Sect. 5 dealing with the Conclusion.

## 2 Literature Survey

The various research works that dealt with false positive reduction in mammographic mass detection have been discussed. Several image processing, deep learning and machine learning techniques had been incorporated in the works.

Sami Dhahbi et al. [8], dealt with the reduction of false positives using mammographic texture analysis in computer-aided mass detection using the region of interests (ROIs) from the DDSM database. Kolmogorov-Smirnov distance, maximum subregion descriptors, Hilbert's image representation, fractal texture analysis and gray level co-occurrence matrix (GLCM) had been employed for extracting the features and random forest classifier had been employed for classification. Khaoula Belhaj Soulami et al. [9], dealt with the classification of mammogram masses as abnormal or normal and normal, malignant or benign using the deep learning capsule network. Density-specific mass detection results had also been analysed. Romesh Laishram et al. [10], dealt with the diagnosis and detection of masses in mammograms from the DDSM and mini-Mammographic Image Analysis Society (MIAS) database. False positive reduction had been done by extracting the multi-gradient local quinary pattern (M-GQP) features and then classifying it as normal or abnormal and malignant or benign. Jayasree Chakraborty et al. [11], dealt with the automatic diagnosis and detection of a mammographic mass from DDSM. GLCM features had been extracted from the suspicious ROIs and were classified as normal or abnormal. Multi-resolution features had also been extracted from the ROIs and were classified as benign or malignant. João Otávio Bandeira Diniz et al. [12] dealt with the implementation of density-adapted mass detection models employing the DDSM database. The density classifier model and the density-adapted mass detection models employed the convolutional neural network (CNN) classifier. Yanfeng
Li et al. [13], dealt with the detection of masses in mammograms by employing the CNN classifier and bilateral analysis. Mammograms from the InBreast databases had been employed and mass detection had been performed using the Siamese-Faster-RCNN network.

From the literature survey it can be inferred that, the development of density-specific models for false positive reduction in mammograms have not been explored much. The incorporation of four breast density categories and machine learning techniques to develop the density-specific models have also not been carried out.

# 3 Proposed Methodology

The proposed work's block diagram is shown in the Fig. 1. Based on breast density type in accordance with the BI-RADS density assessment categories, four density-specific mass detection models have been developed. Each model incorporates the following phases; pre-processing, segmentation, extraction of features, selection of features and classification. The abnormal and normal mammograms undergo pre-processing and the suspicious regions are segmented as region of interests (ROIs). Various features using different detector and descriptor combinations are then extracted from the ROIs. Feature selection has been done to select the significant features that help in an efficient classification. Each density-specific mass detection model performs a binary classification and various machine learning classifiers are used to classify the significant feature set of each ROI as abnormal or normal resulting in false positive reduction. A comparison of the various features and classifiers used is done and in accordance with the performance metrics obtained, the classifier and the feature set that performs the false positive reduction in an efficient manner has been identified for each density-specific mass detection model.

## 3.1 Dataset Description

The mammogram dataset utilized in this work is acquired from the Image Retrieval in Medical Applications (IRMA) version of the Digital Database for Screening Mammography (DDSM) database. The mammograms are in the PNG format and are grayscale images. The dataset consists of abnormal and normal mammograms of both MLO and CC views. This work has employed 624 normal mammograms and 1098 abnormal mammograms from 197 patients and 307 patients, respectively. The number of segmented ROIs employed by the four density-specific mass detection models have been listed in the Table 1.

## 3.2 Data Pre-processing

Data pre-processing enhances the quality of the mammograms for better findings. The connected component labelling method has been employed to perform the label removal as this method aids in the removal of the desired connected component. Here, the desired component to be removed is the label. Following the previous step, pectoral muscle visible in mediolateral oblique (MLO) view mammograms has been removed using the
<figure><img src="image_3.png" /><figcaption>Fig. 1. Block diagram of the proposed methodology.</figcaption></figure>

<table><caption>**Table 1.** Normal and abnormal ROIs of each breast density category.</caption><thead><tr><td>Density category</td><td>Normal ROIs</td><td>Abnormal ROIs</td><td>Total</td></tr></thead><tbody><tr><td>1</td><td>194</td><td>224</td><td>418</td></tr><tr><td>2</td><td>652</td><td>682</td><td>1334</td></tr><tr><td>3</td><td>620</td><td>631</td><td>1251</td></tr><tr><td>4</td><td>293</td><td>263</td><td>446</td></tr></tbody></table>
convex hull method [14]. This muscle is removed precisely from the whole breast area in the mammogram. Due to low quantum counts during the X-ray imaging process, mammograms are prone to quantum noise [15]. Wiener filter with a window size of 5 × 5 has been employed to eliminate the noise in both the MLO and craniocaudal (CC) view mammograms. Contrast limited adaptive histogram equalization (CLAHE) is employed for performing contrast enhancement on the denoised images as this method would enhance even minute details to a great extent that aids further analysis. Segmentation of suspicious regions has been performed using the k-means clustering method. Suspicious regions refer to the dense breast areas that tend to appear like masses in the breast tissue. The k-means method clusters the image on the basis of the intensity level. The 'k' value decides the number of clusters. Each cluster contains a group of pixels with a specific intensity level. From the obtained clusters, connected components containing the brightest pixels are extracted as the suspicious regions, as these regions are dense with high intensity levels. The segmentation algorithm is developed with high sensitivity and this is because, the algorithm is designed with a fixed larger value of 'k' to segment all the suspicious regions irrespective of a normal dense breast tissue resulting in a large number of false positives.

## 3.3 False Positive Reduction in Density-Specific Mass Detection Model

The false positive reduction phase includes the extraction of features from the ROIs that were suspected as masses, followed by the classification of the extracted ROI as abnormal or normal. Various features have been extracted from the ROIs employing several detector and descriptor combinations that include KAZE, scale invariant feature transform (SIFT), speeded-up robust features (SURF), binary robust independent elementary features (BRIEF), oriented fast and rotated brief (ORB) and binary robust invariant scalable keypoints (BRISK). A detector detects the most prominent feature points in an abnormal and normal image and a descriptor describes the detected feature points and forms feature vector. The dimension of a feature vector is 1 × 500. Local binary pattern (LBP), gray level co-occurrence matrix (GLCM), histogram statistical and gabor features are also extracted with vectors of dimension 1 × 59, 1 × 5, 1 × 5 and 1 × 2 respectively. A total of 14 features have been utilized and a comparative analysis is done on the basis of their performance for each density-specific mass detection model. The various detector and descriptor combinations [16] employed for extracting the features apart from LBP, GLCM, gabor and histogram statistical features are listed in Table 2.

Feature selection process involves the selection of the prominent features from the entire feature set using the method analysis of variance (ANOVA). The method works on the basis of the F-scores assigned to each feature [17]. A comparison of all the features is done and the feature performing the classification of abnormal and normal breast tissues efficiently has been identified. Classifiers like Support vector machine (SVM), K-nearest neighbours (KNN), Random forest, Decision tree, Naïve bayes, Logistic regression, Extra trees classifier, Adaptive boosting, Gradient boosting and Multi-layer perceptron (MLP) have been utilized for performing the classification task in all the four mass detection models that results in false positive reduction. The selected features undergo min-max normalization prior to classification. K-fold cross validation is done and the
<table><caption>**Table 2.** Various detector and descriptor combinations.</caption><thead><tr><td>Detector</td><td>Descriptor</td><td>Detector</td><td>Descriptor</td></tr></thead><tbody><tr><td>KAZE</td><td>KAZE</td><td>SIFT</td><td>SIFT</td></tr><tr><td>KAZE</td><td>SURF</td><td>SIFT</td><td>KAZE</td></tr><tr><td>KAZE</td><td>SIFT</td><td>BRIEF</td><td>SURF</td></tr><tr><td>SURF</td><td>SURF</td><td>BRISK</td><td>SURF</td></tr><tr><td>SURF</td><td>KAZE</td><td>ORB</td><td>ORB</td></tr></tbody></table>

hyperparameter tuning is also done to acquire the efficient parameters to perform classification. A comparison between the classifiers used is done. The classifier performing an efficient classification is identified and this identification is in accordance with the metrics of each classifier's performance. The performance metric here denotes the classification accuracy of each classifier.

# 4 Results and Discussion

## 4.1 Pre-processing and Segmentation

The pre-processing steps performed on the abnormal and normal mammograms of both MLO and CC views are removal of label, removal of pectoral muscle, removal of noise and contrast enhancement. In the segmentation phase of each model, suspicious regions from the abnormal and normal pre-processed images have been segmented. The extracted suspicious regions are the ROIs that have been utilized by the forthcoming phases in the mass detection models. The results of the pre-processing and segmentation phase for CC and MLO view mammograms are displayed in the Fig. 2 (a–h) and Fig. 3 (a–i) respectively.

## 4.2 False Positive Reduction in Density-Specific Mass Detection Model

A comparison of the features used in the density-specific mass detection models 1, 2, 3 and 4 has been depicted in the Table 3.

A comparison of the classifiers used has also been done for all the four models to analyze the performance of the model. This has been done by feeding the classifiers with the feature that produced the highest classification accuracy. The classifiers comparative analysis results for the models 1, 2, 3 and 4 has been depicted in the Table 4.

From the results obtained it can be inferred that, the KAZE features, KAZE_SIFT features, SIFT_KAZE features and KAZE features extracted from the normal and abnormal ROIs of density categories 1, 2, 3 and 4 respectively have been classified efficiently by the SVM classifier resulting in the classification accuracies of 96%, 80%, 76% and 88% respectively. For each classifier, the decision making is based on the class with the highest probabilistic value given by the Eq. (1).

$$ \max\{P(W_i|X_j, C_i)\} \qquad (1) $$
<figure><img src="image_4.png" /><figcaption>Fig. 2. (a) Abnormal original image (b) Label removed image (c) Noise removed image (d) Contrast enhanced image (e) Segmented image (f) Suspicious regions image (g) A suspicious region marked as an ROI image (h) Extracted ROI image.</figcaption></figure>

where, {$P(W_i | X_j, C_i)$} is the probability obtained by the classifier $C_i$ for instance $X_j$ and $W_i$ is the predicted class.

Among the classifiers, the one with the highest classification accuracy is chosen and this is given by the Eq. (2).

$$ \max\{\text{Accuracy}(C_i)\} \qquad (2) $$

where, $C_i$ represents the classifier.

Various performance metrics like confusion matrix, F1-score, precision, recall and receiver operating characteristic curve (ROC) have also been obtained to analyze the performance of the density-specific mass detection models.

Tables 5, 6, 7 and 8 depicts the confusion matrix of the density category 1, 2, 3 and 4 mass detection models respectively. At the end of the classification, 39 out of 39 normal images and 42 out of 45 abnormal images belonging to the density category 1, 96 out of 130 normal images and 117 out of 136 abnormal images belonging to the density category 2, 93 out of 124 normal images and 97 out of 126 abnormal images belonging to the density category 3 and 52 out of 59 normal images and 46 out of 53 abnormal
<figure><img src="image_5.png" /><figcaption>**Fig. 3.** (a) Normal original image (b) Label removed image (c) Pectoral muscle removed image (d) Noise removed image (e) Contrast enhanced image (f) Segmented image (g) Suspicious regions image (h) A suspicious region marked as an ROI image (i) Extracted ROI image.</figcaption></figure>

images belonging to the density category 4 have been classified correctly, resulting in an overall classification accuracy of 96%, 80%, 76% and 88% respectively.

Various other performance metrics obtained for the four mass detection models are depicted in the Table 9. Figure 4, 5, 6 and 7 depicts the ROC curve and an area under curve (AUC) value of 1.00, 0.88, 0.80 and 0.96 has been obtained for each density-specific mass detection model respectively.

In this work, four density-specific mass detection models have been developed as it is difficult for a single classifier model to learn the tissue characteristics of all the density types and this also affects the mass detection performance. A comparative analysis between the mass detection results obtained for the four density-specific mass detection models and the mass detection model that is not density-specific has been performed. A single non-density specific mass detection model has been developed and the classification has been performed on the same set of normal and abnormal ROIs that had been used for the development of the four density-specific mass detection models. About 1759 normal ROIs and 1800 abnormal ROIs irrespective of their density types have been used by the non-density specific mass detection model. At the end of the classification phase, KAZE features extracted from the normal and abnormal ROIs have been efficiently classified using the SVM classifier and this results in an overall classification accuracy of
<table><caption>**Table 3.** Comparative analysis of the features.</caption><thead><tr><th>Feature<br>(Detector Corporation)</th><th>Density_1<br>Model<br>Accuracy</th><th>Density_2<br>Model<br>Accuracy</th><th>Density_3<br>Model<br>Accuracy</th><th>Density_4<br>Model<br>Accuracy</th></tr></thead><tbody><tr><td><strong>KAZE</strong></td><td><strong>96%</strong></td><td>77%</td><td>72%</td><td><strong>88%</strong></td></tr><tr><td>KAZE_SURF</td><td>95%</td><td>68%</td><td>64%</td><td>78%</td></tr><tr><td>SIFT</td><td>94%</td><td>75%</td><td>70%</td><td>73%</td></tr><tr><td><strong>SIFT_KAZE</strong></td><td>93%</td><td>77%</td><td><strong>76%</strong></td><td>85%</td></tr><tr><td><strong>KAZE_SIFT</strong></td><td>92%</td><td><strong>80%</strong></td><td>75%</td><td>76%</td></tr><tr><td>SURF_KAZE</td><td>90%</td><td>79%</td><td>75%</td><td>87%</td></tr><tr><td>SURF</td><td>89%</td><td>68%</td><td>62%</td><td>77%</td></tr><tr><td>BRISK_SURF</td><td>85%</td><td>63%</td><td>62%</td><td>73%</td></tr><tr><td>BRIEF_SURF</td><td>82%</td><td>64%</td><td>60%</td><td>73%</td></tr><tr><td>LBP</td><td>80%</td><td>72%</td><td>67%</td><td>70%</td></tr><tr><td>ORB</td><td>79%</td><td>67%</td><td>66%</td><td>75%</td></tr><tr><td>GLCM</td><td>71%</td><td>63%</td><td>65%</td><td>71%</td></tr><tr><td>Gabor</td><td>63%</td><td>54%</td><td>52%</td><td>57%</td></tr><tr><td>Histogram Statistical</td><td>55%</td><td>55%</td><td>64%</td><td>68%</td></tr></tbody></table>

<table><caption>**Table 4.** Comparative analysis of the classifiers.</caption><thead><tr><th>Classifier</th><th>Density_1<br>Model<br>Accuracy</th><th>Density_2<br>Model<br>Accuracy</th><th>Density_3<br>Model<br>Accuracy</th><th>Density_4<br>Model<br>Accuracy</th></tr></thead><tbody><tr><td><strong>SVM</strong></td><td><strong>96%</strong></td><td><strong>80%</strong></td><td><strong>76%</strong></td><td><strong>88%</strong></td></tr><tr><td>Gradient boosting</td><td>96%</td><td>75%</td><td>73%</td><td>83%</td></tr><tr><td>Multilayer perceptron</td><td>95%</td><td>77%</td><td>68%</td><td>87%</td></tr><tr><td>Logistic regression</td><td>94%</td><td>72%</td><td>70%</td><td>86%</td></tr><tr><td>Random forest</td><td>93%</td><td>69%</td><td>69%</td><td>73%</td></tr><tr><td>Naïve bayes</td><td>93%</td><td>63%</td><td>61%</td><td>66%</td></tr><tr><td>Adaboost</td><td>90%</td><td>74%</td><td>68%</td><td>81%</td></tr><tr><td>Extra trees classifier</td><td>88%</td><td>62%</td><td>68%</td><td>71%</td></tr><tr><td>Decision tree</td><td>86%</td><td>61%</td><td>66%</td><td>63%</td></tr><tr><td>KNN</td><td>81%</td><td>62%</td><td>56%</td><td>66%</td></tr></tbody></table>
<table><caption>**Table 5.** Confusion matrix – model 1.</caption><thead><tr><th rowspan="2">Predicted class</th><th colspan="3">Actual class</th></tr><tr><th>Class</th><th>1</th><th>0</th></tr></thead><tbody><tr><td></td><td>1</td><td><strong>42</strong></td><td>0</td></tr><tr><td></td><td>0</td><td>3</td><td><strong>39</strong></td></tr></tbody></table>

<table><caption>**Table 6.** Confusion matrix – model 2.</caption><thead><tr><th rowspan="2">Predicted class</th><th colspan="3">Actual class</th></tr><tr><th>Class</th><th>1</th><th>0</th></tr></thead><tbody><tr><td></td><td>1</td><td><strong>117</strong></td><td>34</td></tr><tr><td></td><td>0</td><td>19</td><td><strong>96</strong></td></tr></tbody></table>

<table><caption>**Table 7.** Confusion matrix – model 3.</caption><thead><tr><th rowspan="2">Predicted class</th><th colspan="3">Actual class</th></tr><tr><th>Class</th><th>1</th><th>0</th></tr></thead><tbody><tr><td></td><td>1</td><td><strong>97</strong></td><td>31</td></tr><tr><td></td><td>0</td><td>29</td><td><strong>93</strong></td></tr></tbody></table>

<table><caption>**Table 8.** Confusion matrix – model 4.</caption><thead><tr><th rowspan="2">Predicted class</th><th colspan="3">Actual class</th></tr><tr><th>Class</th><th>1</th><th>0</th></tr></thead><tbody><tr><td></td><td>1</td><td><strong>46</strong></td><td>7</td></tr><tr><td></td><td>0</td><td>7</td><td><strong>52</strong></td></tr></tbody></table>

79%. Table 10 depicts the confusion matrix of the non-density specific mass detection model. After the classification, 304 out of 360 abnormal ROIs and 259 out of 352 normal ROIs have been correctly classified, resulting in an overall classification accuracy of 79%. Various other performance metrics obtained for the non-density specific mass detection model are depicted in the Table 11 and Fig. 8 depicts the ROC curve obtained. AUC value of 0.86 has been obtained for this model.

A comparative analysis between the number of wrongly classified abnormal and normal ROIs obtained for the four density-specific mass detection models and the non-density specific mass detection model has been depicted in Table 12. In a CAD system, segmentation will have high sensitivity and hence a normal breast image without the
<table><caption>**Table 9.** Performance metrics of density-specific mass detection models.</caption><thead><tr><th>Classifier</th><th>Density_1<br>Model<br>Accuracy</th><th>Density_2<br>Model<br>Accuracy</th><th>Density_3<br>Model<br>Accuracy</th><th>Density_4<br>Model<br>Accuracy</th></tr></thead><tbody><tr><td>Accuracy</td><td>96%</td><td>80%</td><td>76%</td><td>88%</td></tr><tr><td>Precision</td><td>0.93, 1.00</td><td>0.83, 0.77</td><td>0.76, 0.76</td><td>0.88, 0.87</td></tr><tr><td>Recall</td><td>1.00, 0.93</td><td>0.74, 0.86</td><td>0.75, 0.77</td><td>0.88, 0.87</td></tr><tr><td>F1-score</td><td>0.96, 0.97</td><td>0.78, 0.82</td><td>0.76, 0.76</td><td>0.88, 0.87</td></tr></tbody></table>

<figure><img src="image_6.png" /><figcaption>**Fig. 4.** Roc curve – model 1.</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>**Fig. 5.** Roc curve – model 2.</figcaption></figure>

presence of mass has a chance of being classified as a breast image with mass and this leads to a rise in false positive rate and so the CAD model must incorporate the false positive reduction stage to reduce the false positive count. A comparative analysis between the false positive rates (FPR) obtained for the four density-specific mass detection models and the non-density specific mass detection model for each density category has also been depicted in Table 12. In the table, four breast density categories, BI-RADS I, II, III and IV are denoted by the terms BD 1, BD 2, BD 3 and BD 4 respectively.

From Table 12 it is inferred that, a total of 130 normal and abnormal images have been misclassified by the four-density specific mass detection models and a total of 149 normal and abnormal images have been misclassified by the non-density specific mass detection model. Density category_1 mass detection model deals with fatty breast tissue mammograms. After the segmentation phase itself, the model will produce a smaller number of false positives resulting in an approximate identification of mass. Further, when these mammograms undergo false positive reduction, they result in zero false positives. This ‘0’ FPR has been achieved in this phase by using the KAZE features
<figure><img src="image_8.png" /><figcaption>Fig. 6. Roc curve – model 3.</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>Fig. 7. Roc curve – model 4.</figcaption></figure>

<table><caption>**Table 10.** Confusion matrix – non-density specific mass detection model.</caption><thead><tr><th rowspan="2">Predicted class</th><th colspan="3">Actual class</th></tr><tr><th>Class</th><th>1</th><th>0</th></tr></thead><tbody><tr><td></td><td>1</td><td><strong>97</strong></td><td>31</td></tr><tr><td></td><td>0</td><td>29</td><td><strong>93</strong></td></tr></tbody></table>

<table><caption>**Table 11.** Performance metrics – non-density specific mass detection model.</caption><thead><tr><th>Performance metrics</th><th>Results obtained</th></tr></thead><tbody><tr><td>Accuracy</td><td>79%</td></tr><tr><td>Precision</td><td>0.82, 0.77</td></tr><tr><td>Recall</td><td>0.74, 0.84</td></tr><tr><td>F1-score</td><td>0.78, 0.80</td></tr></tbody></table>

extracted from the images. The FPR values of each density category in the density-specific expert models for mass detection is lower than the FPR values of each density category in the non-density specific expert model for mass detection indicating that density-specific expert models outperform the non-density specific expert model for mass detection.
<figure><img src="image_10.png" /><figcaption>Fig. 8. ROC curve – non-density specific mass detection model.</figcaption></figure>

<table><caption>**Table 12.** Comparative analysis between four density-specific mass detection models and a non-density specific mass detection model.</caption><thead><tr><th colspan="4">Four density-specific mass detection models</th><th colspan="4">Single non-density specific mass detection model</th></tr></thead><tbody><tr><td colspan="8">Number of misclassified images in each density category</td></tr><tr><td>BD 1</td><td>BD 2</td><td>BD 3</td><td>BD 4</td><td>BD 1</td><td>BD 2</td><td>BD 3</td><td>BD 4</td></tr><tr><td>3</td><td>53</td><td>60</td><td>14</td><td>7</td><td>58</td><td>70</td><td>14</td></tr><tr><td colspan="8">False positive rate (FPR) of each mass detection model</td></tr><tr><td>BD 1</td><td>BD 2</td><td>BD 3</td><td>BD 4</td><td>BD 1</td><td>BD 2</td><td>BD 3</td><td>BD 4</td></tr><tr><td>0</td><td>0.26</td><td>0.25</td><td>0.12</td><td>0.12</td><td>0.34</td><td>0.27</td><td>0.14</td></tr></tbody></table>

## 5 Conclusion

The development of density-specific expert models for false positive reduction in mammographic mass detection has been proposed and implemented. The abnormal and normal mammograms from the Image Retrieval in Medical Applications (IRMA) version of the Digital Database for Screening Mammography (DDSM) database have been pre-processed and the regions suspected to be masses have been segmented and extracted as region of interests (ROIs) and then features were extracted from them using various detector and descriptor combinations and were further classified as abnormal or normal resulting in the false positive reduction. Each density-specific mass detection model has been incorporated with the mentioned phases resulting in the classification accuracies of 96%, 80%, 76% and 88% and false positive rates (FPR) of 0, 0.26, 0.25 and 0.12 using
the KAZE, KAZE_SIFT, SIFT_KAZE and KAZE features for the four mass detection models respectively.

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