Mammographic Mass Detection Research Articles

Improving Mass Detection in Mammography Using Focal Loss Based RetinaNet

Breast cancer is a significant global health issue and plays a crucial role in improving patient outcomes through early detection. This study aims to enhance the accuracy and efficiency of breast cancer diagnosis by investigating the application of the RetinaNet and Faster R-CNN algorithms for mass detection in mammography images. A specialized dataset was created for mass detection from mammography images and validated by an expert radiologist. The dataset was trained using RetinaNet and Faster R-CNN, a state-of-the-art object detection model. The training and testing were conducted using the Detectron2 platform. To avoid overfitting during training, data augmentation techniques available in the Detectron2 platform were used. The model was tested using the AP50, precision, recall, and F1-Score metrics. The results of the study demonstrate the success of RetinaNet in mass detection. According to the obtained results, an AP50 value of 0.568 was achieved. The precision and recall performance metrics are 0.735 and 0.60 respectively. The F1-Score metric, which indicates the balance between precision and recall, obtained a value of 0.66. These results demonstrate that RetinaNet can be a potential tool for breast cancer screening and has the potential to provide accuracy and efficiency in breast cancer diagnosis. The trained RetinaNet model was integrated into existing PACS (Picture Archiving and Communication System) systems and made ready for use in healthcare centers.

Enhanced breast mass mammography classification approach based on pre-processing and hybridization of transfer learning models.

The second most prevalent cause of death among women is now breast cancer, surpassing heart disease. Mammography images must accurately identify breast masses to diagnose early breast cancer, which can significantly increase the patient's survival percentage. Although, due to the diversity of breast masses and the complexity of their microenvironment, it is still a significant issue. Hence, an issue that researchers need to continue searching into is how to establish a reliable breast mass detection approach in an effective factor application to increase patient survival. Even though several machine and deep learning-based approaches were proposed to address these issues, pre-processing strategies and network architectures were insufficient for breast mass detection in mammogram scans, which directly influences the accuracy of the proposed models. Aiming to resolve these issues, we propose a two-stage classification method for breast mass mammography scans. First, we introduce a pre-processing stage divided into three sub-strategies, which include several filters for Region Of Interest (ROI) extraction, noise removal, and image enhancements. Secondly, we propose a classification stage based on transfer learning techniques for feature extraction, and global pooling for classification instead of standard machine learning algorithms or fully connected layers. However, instead of using the traditional fine-tuning feature extraction phase, we proposed a hybrid model where we concatenate two recent pre-trained CNNs to assist the feature extraction phase, rather than using one. Using the CBIS-DDSM dataset, we managed to increase mainly each of the accuracy, sensitivity, and specificity reaching the highest accuracy of 98,1% using the Median filter for noise removal. Followed by the Gaussian filter trial with 96% accuracy, meanwhile, the winner filter attained the lowest accuracy of 94.13%. Moreover, the usage of global average pooling as a classifier is suitable in our case better than global max pooling. The experimental findings demonstrate that the suggested strategy of breast Mass detection in mammography can outperform the top-ranked methods currently in use in terms of classification performance.

Fully convolutional network for automated detection and diagnosis of mammographic masses.

Breast cancer, though rare in male, is very frequent in female and has high mortality rate which can be reduced if detected and diagnosed at the early stage. Thus, in this paper, deep learning architecture based on U-Net is proposed for the detection of breast masses and its characterization as benign or malignant. The evaluation of the proposed architecture in detection is carried out on two benchmark datasets- INbreast and DDSM and achieved a true positive rate of 99.64% at 0.25 false positives per image for INbreast dataset while the same for DDSM are 97.36% and 0.38 FPs/I, respectively. For mass characterization, an accuracy of 97.39% with an AUC of 0.97 is obtained for INbreast while the same for DDSM are 96.81%, and 0.96, respectively. The measured results are further compared with the state-of-the-art techniques where the introduced scheme takes an edge over others.

Domain generalization in deep learning based mass detection in mammography: A large-scale multi-center study

Computer-aided detection systems based on deep learning have shown great potential in breast cancer detection. However, the lack of domain generalization of artificial neural networks is an important obstacle to their deployment in changing clinical environments. In this study, we explored the domain generalization of deep learning methods for mass detection in digital mammography and analyzed in-depth the sources of domain shift in a large-scale multi-center setting. To this end, we compared the performance of eight state-of-the-art detection methods, including Transformer based models, trained in a single domain and tested in five unseen domains. Moreover, a single-source mass detection training pipeline was designed to improve the domain generalization without requiring images from the new domain. The results show that our workflow generalized better than state-of-the-art transfer learning based approaches in four out of five domains while reducing the domain shift caused by the different acquisition protocols and scanner manufacturers. Subsequently, an extensive analysis was performed to identify the covariate shifts with the greatest effects on detection performance, such as those due to differences in patient age, breast density, mass size, and mass malignancy. Ultimately, this comprehensive study provides key insights and best practices for future research on domain generalization in deep learning based breast cancer detection.

Breast Masses Detection and Segmentation in Full-Field Digital Mammograms using Unified Convolution Neural Network.

Breast Cancer has been the primary reason for mortality in women of age between twenties and sixties worldwide; moreover early detection and treatment provides patients to get absolute treatment and decrease the mortality rate. Furthermore, recent research indicates that most experienced physicians have plenty of limitations, hence the plethora of work has been carried out to develop an automated mechanism of segmentation and classification of affected area and type of cancer; however, it is still considered to be highly challenging due to the variability of tumor in shape, low signal to noise ratio, shape, size and location of tumor. Furthermore, mammographic mass segmentation and detection are performed as a separate task and a convolution neural network is a highly adopted architecture for the same. In this research, we have designed and developed unified CNN architecture to perform the segmentation and detection of a breast mass. The unified-CNN architecture comprises a novel module for convolution which is combined through additional offset. Further RRS aka Random Region Selection mechanism is applied for data augmentation approach and high-level feature map is implied to achieve the high prediction. Furthermore, unified-CNN is evaluated using the metrics like true positive Rate at FPI (False Positive per Image) and Dice Index on INBreast dataset, also comparative analysis is out carried with various existing methodology. Unified-CNN is developed through improvising CNN. It introduces a novel module at the convolution layer to aim for a high-level feature map in order to get a high prediction. RRS (Random Region Selection) algorithm is used as the data augmentation approach to select the boundary region of the affected area; further robust model training is designed and optimized for process to make optimal. Unified-CNN introduces novel module at the convolution layer to aim for high level feature map in order to get high prediction; further ROI pooling is utilized for boundary detection in images.

WDO optimized detection for mammographic masses and its diagnosis: A unified CAD system

For decades, breast cancer is the leading cause of cancer-related deaths among women. Early detection and diagnosis of breast cancer can effectively reduce the mortality rate. Masses are one of the manifestations of imperceptible breast cancer visible in mammograms whose detection and diagnosis is a challenging task due to its subtle nature. In this paper, a unified computer-aided detection/diagnosis scheme is proposed for the automatic detection and diagnosis of mammographic masses. Being simple and effective, multilevel image thresholding based on Otsu’s method is considered for localization of suspicious mass lesions, but its performance is compromised while defining the precise threshold value especially when the intensity profile in and around the anomalies does not vary much. To mitigate the issue, a nature-inspired optimization algorithm, wind driven optimization, is proposed which in combination with Otsu’s multilevel thresholding identifies the potential candidates of mass lesions. To reduce the false positives and diagnose the malignant masses, a texture-based multi-gradient local quinary pattern (M-GQP) feature is introduced which renders higher consistency in computing gradient information from the uniform and near-uniform areas. Due to eight distinct Sobel masks, it offers a better resolution in edge regions in addition to the micro information at different orientations. Four popular classifiers (support vector machine, Fisher’s linear discriminant, K-nearest neighbor, and an ensemble classifier) are also investigated to discriminate malignant tumors from benign lesions and normal tissues. The proposed integrated approach for the detection and characterization of mammographic masses is evaluated on two benchmark databases — mini-MIAS and DDSM comprising of 68 and 500 mammograms, respectively and achieved a sensitivity of 96.9% and 96.2% with 0.09 and 0.17 false positives per image after false positive reduction for the respective datasets. The diagnosis of malignant masses prior to and following false-positive reduction observes an Az value of 0.98 and 0.94 with an accuracy of 99.04% and 92.65%, respectively for the mini-MIAS dataset while the same for the DDSM are 0.99 and 0.92 with an accuracy of 98.33% and 78.50%, respectively. In a three-class classification (normal, benign and malignant), an accuracy of 99.04% and 97.76% with an F1 score of 0.98 and 0.97 for malignancy is obtained for the mini-MIAS and DDSM images, respectively which are promising in nature when compared to other competing schemes in the state-of-the-art.

Breast Mass Detection in Mammography Based on Image Template Matching and CNN.

In recent years, computer vision technology has been widely used in the field of medical image processing. However, there is still a big gap between the existing breast mass detection methods and the real-world application due to the limited detection accuracy. It is known that humans locate the regions of interest quickly and further identify whether these regions are the targets we found. In breast cancer diagnosis, we locate all the potential regions of breast mass by glancing at the mammographic image from top to bottom and from left to right, then further identify whether these regions are a breast mass. Inspired by the process of human detection of breast mass, we proposed a novel breast mass detection method to detect breast mass on a mammographic image by stimulating the process of human detection. The proposed method preprocesses the mammographic image via the mathematical morphology method and locates the suspected regions of breast mass by the image template matching method. Then, it obtains the regions of breast mass by classifying these suspected regions into breast mass and background categories using a convolutional neural network (CNN). The bounding box of breast mass obtained by the mathematical morphology method and image template matching method are roughly due to the mathematical morphology method, which transforms all of the brighter regions into approximate circular areas. For regression of a breast mass bounding box, the optimal solution should be searched in the feasible region and the Particle Swarm Optimization (PSO) is suitable for solving the problem of searching the optimal solution within a certain range. Therefore, we refine the bounding box of breast mass by the PSO algorithm. The proposed breast mass detection method and the compared detection methods were evaluated on the open database Digital Database for Screening Mammography (DDSM). The experimental results demonstrate that the proposed method is superior to all of the compared detection methods in detection performance.

Automated mammographic mass detection using deformable convolution and multiscale features.

Designing computer-assisted diagnosis (CAD) systems that can precisely identify lesions from mammography images would be useful for clinicians. Considering the morphological variation in breast cancer, it is necessary to extract robust features from the mammogram. Here, we propose a mass detection CAD system that is based on Faster R-CNN. First, we applied a novel convolution network in the backbone of Faster R-CNN, namely deformable convolution network (DCN), which improves the detection of lesions with varying shapes and sizes. Second, the original Faster R-CNN uses the output of the last layer of the backbone as a single-scale feature map. To facilitate the detection of small lesions, we used a multiscale feature pyramid network of multiple cross-scale connections between the different output layers of the backbone, called the neural architecture search-feature pyramid network (NAS-FPN). Thus, we were able to integrate the best features into the model. We then evaluated our method by using the datasets the Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM) and INbreast, respectively. Our method yielded a true positive rate of 0.9345 at 2.2805 false positive per image on CBIS-DDSM and a true positive rate of 0.9554 at 0.3829 false positive per image on INbreast. Graphical abstract.

Fusion of Orthogonal Moment Features for Mammographic Mass Detection and Diagnosis

Masses are mammographic nonpalpable signs of breast cancer. These masses could be detected using screening mammography. This paper proposed a system utilizing orthogonal moment invariants (OMIs) features for mammographic masses detection and diagnosis. In this work, three sets of OMIs features were extracted. These OMIs features are Gaussian-Hermite moments (GHMs), Gegenbauer moments (GeMs), and Legendre moments (LMs). The extracted features are fused and presented to the particle swarm optimization (PSO) algorithm for feature selection. The classification step is achieved using the support vector machine (SVM). The proposed system is evaluated using 400 regions, extracted from the DDSM dataset. The obtained results reveal the promising application of OMIs features for masses detection and identification. It shows that fusing the OMIs features produces an acceptable detection performance where the area under the receiver operating characteristics (ROC) curve is $Az=0.969\pm 0.01$ and the best performance of OMIs features is $Az = 0.856\pm 0.053$ for characterizing the malignancy of masses.

Multi-scale sifting for mammographic mass detection and segmentation

Breast mass detection and segmentation are challenging tasks due to the fact that breast masses vary in size and appearance. In this work, we present a simultaneous detection and segmentation scheme for mammographic lesions that is constructed in a sifting architecture. It utilizes a novel region candidate selection approach and cascaded learning techniques to achieve state-of-the-art results while handling a high class imbalance. The region candidates are generated by a novel multi-scale morphological sifting (MMS) approach, where oriented linear structuring elements are used to sieve out the mass-like objects in mammograms including stellate patterns. This method can accurately segment masses of various shapes and sizes from the background tissue. To tackle the class imbalance problem, two different ensemble learning methods are utilized: a novel self-grown cascaded random forests (CasRFs) and the random under-sampling boost (RUSBoost). The CasRFs is designed to handle class imbalance adaptively using a probability-ranking based under-sampling approach, while RUSBoost uses a random under-sampling technique. This work is evaluated on two publicly available datasets: INbreast and DDSM BCRP. On INbreast, the proposed method achieves an average sensitivity of 0.90 with 0.9 false positives per image (FPI) using CasRFs and with 1.2 FPI using RUSBoost. On DDSM BCRP, the method yields a sensitivity of 0.81 with 3.1 FPI using CasRFs and with 2.9 FPI using RUSboost. The performance of the proposed method compares favorably to the state-of-the-art methods on both datasets, especially on highly spiculated lesions.

Breast Cancer Detection using Crow Search Optimization based Intuitionistic Fuzzy Clustering with Neighborhood Attraction

Objective:Generally, medical images contain lots of noise that may lead to uncertainty in diagnosing the abnormalities. Computer aided diagnosis systems offer a support to the radiologists in identifying the disease affected area. In mammographic images, some normal tissues may appear to be similar to masses and it is tedious to differentiate them. Therefore, this paper presents a novel framework for the detection of mammographic masses that leads to early diagnosis of breast cancer.Methods:This work proposes a Crow search optimization based Intuitionistic fuzzy clustering approach with neighborhood attraction (CrSA-IFCM-NA) for identifying the region of interest. First order moments were extracted from preprocessed images. These features were given as input to the Intuitionistic fuzzy clustering algorithm. Instead of randomly selecting the initial centroids, crow search optimization technique is applied to choose the best initial centroid and the masses are separated. Experiments are conducted over the images taken from the Mammographic Image Analysis Society (mini-MIAS) database.Results:CrSA-IFCM-NA effectively separated the masses from mammogram images and proved to have good results in terms of cluster validity indices indicating the clear segmentation of the regions.Conclusion:The experimental results show that the accuracy of the proposed method proves to be encouraging for detection of masses. Thus, it provides a better assistance to the radiologists in diagnosing breast cancer at an early stage.

Transfer Representation Learning using Inception-V3 for the Detection of Masses in Mammography.

Breast cancer is the most prevalent cancer among women. The most common method to detect breast cancer is mammography. However, interpreting mammography is a challenging task that requires high skills and is timeconsuming. In this work, we propose a computer-aided diagnosis (CAD) scheme for mammography based on transfer representation learning using the Inception-V3 architecture. We evaluate the performance of the proposed scheme using the INBreast database, where the features are extracted from different layers of the architecture. In order to cope with the small dataset size limitation, we expand the training dataset by generating artificial mammograms and employing different augmentation techniques. The proposed scheme shows great potential with a maximal area under the receiver operating characteristics curve of 0.91.

Computer-Aided Detection of Mammographic Masses Using Hybrid Region Growing Controlled by Multilevel Thresholding

Masses are one of the common signs of nonpalpable breast cancer visible in mammograms. However, due to its irregular and obscured margin, variability in size, and occlusion within dense breast tissue, a mass may be missed during screening. In this paper, we propose a novel approach for automatic detection of mammographic masses using an iterative method of multilevel high-to-low intensity thresholding, followed by region growing and reduction of false positives, in which an image is considered as a 3D topographic map with intensity as the third dimension. At each iteration, first, the focal regions of masses are obtained by thresholding, and then potential sites of masses are extracted from the focal regions with a newly developed region growing technique. Finally, false positives are reduced using contrast and distance between two potential mass regions, and by using a classifier after the extraction of shape- and orientation-based features. The performance of the method is evaluated with 120 scanned-film images, including 55 images with 57 masses and 65 normal images from the mini-MIAS database; 555 scanned-film images, including 355 images with 370 masses and 200 normal images from the DDSM; and 219 digital radiography (DR) images, including 99 images with 120 masses and 120 normal images from a local database. For the mini-MIAS, DDSM, and DR images 90% sensitivity is achieved at a rate of 4.4, 0.99, and 1.0 false positive per images, respectively.

Computer-aided detection and diagnosis of mammographic masses using multi-resolution analysis of oriented tissue patterns

In this article, a novel approach is proposed for automatic detection and diagnosis of mammographic masses, one of the common signs of non-palpable breast cancer. However, detection and diagnosis of mass are difficult due to its irregular shape, variability in size, and occlusion within breast tissue. The main aim of this study is to classify masses into benign and malignant after detecting them automatically. We propose an iterative method of high-to-low intensity thresholding controlled by radial region growing for the detection of masses. Based on the observation that in presence of mass orientation of tissue patterns changes, which may differ from benign to malignant, a multi resolution analysis of orientation of tissue patterns is then performed to categorize them. The performance of the proposed algorithm is evaluated with images from the digital database for screening mammography (DDSM), containing 450 benign masses, 440 malignant masses, and 410 normal images. A sensitivity of 85.0% is achieved at 1.4 false positives per image in mass detection, whereas an area under the receiver operating characteristic curve of 0.92 with an accuracy of 83.30% is achieved for the diagnosis of malignant masses.

A cost-sensitive Bayesian combiner for reducing false positives in mammographic mass detection.

Mammography is the most widely used modality for early breast cancer detection. This work proposes a new computer-aided mass detection approach, in which a denoising method called BM3D is first applied to mammograms. Afterwards, using an adaptive segmentation algorithm, images are segmented to suspicious regions of interest (ROIs) and then a classifier is used to understand the features of true positive (TP) and false positive (FP) patterns. In this way, from selected suspicious ROIs, fractal dimension, texture and intensity features are extracted. Subsequently, a discretization approach followed by correlation-based feature selection (CFS) is combined with a genetic algorithm to obtain the most representative features. To neutralize the classifier's bias in favor of the major class in imbalanced datasets, an oversampling algorithm is used. In the next step, a cost-sensitive ensemble classifier based on a trainable combiner is proposed in order to reduce the number of FP samples. Finally, the presented method is validated on miniMIAS and INBreast datasets. The free-response receiver operating characteristic (FROC) analysis results prove the efficiency of the proposed approach. A sensitivity of 88% and false positive per image (FPpI) of 0.78 for miniMIAS and also a sensitivity of 86% and FPpI of 0.75 for INBreast dataset were obtained.

Threshold Based Segmentation Technique for Mass Detection in Mammography

Threshold Based Segmentation Technique for Mass Detection in Mammography

Computer aided mass detection in mammography with temporal change analysis

This paper presents a method to extract change information from temporal mammogram pairs and to incorporate the temporal change information in the malignant mass classification. In this method, a temporal mammogram registration framework which is based on spatial relations between regions of interest and graph matching was used to create correspondences between regions of current mammogram and regions of previous mammogram. 18 image features were then used to capture the differences (temporal changes) between the matched regions. To assess the contribution of temporal change information to the mass detection, 5 methods were designed to combine mass classification on image features measured on single regions and mass classification on temporal features to improve overall mass classification. The method was tested on 95 pairs of temporal mammograms using k-fold cross validation procedure. The experimental results showed that, when combining two classification results using linear combination or by taking minimum value, the Az score of overall classification performance increased from 0.8843 to 0.8989 and 0.8863 respectively. The results demonstrated that registering temporal mammograms, measuring temporal changes from matched regions and incorporating the change information in the mass classification improves the overall mass detection.

FAST DETECTION OF MASSES IN MAMMOGRAMS WITH DIFFICULT CASE EXCLUSION

Breast cancer is one of the most common forms of cancer among women. Currently mammography is the most efficient method for early detection. A simple and fast mammographic mass detection system and two different methods for difficult case exclusion are presented in this paper. The mass detection system uses a modified version of a known algorithm for small masses and a new algorithm for large masses. The first difficult case filtering method is based on tissue density estimation, the second one on mass candidate count. The system was tested with 600 mammographic cases, each containing 4 images. Case-level performance was measured for malignant mass detection first without and then with difficult case exclusion.

An Automatic Mass Detection System in Mammograms Based on Complex Texture Features

It is difficult for radiologists to identify the masses on a mammogram because they are surrounded by complicated tissues. In current breast cancer screening, radiologists often miss approximately 10-30% of tumors because of the ambiguous margins of lesions and visual fatigue resulting from long-time diagnosis. For these reasons, many computer-aided detection (CADe) systems have been developed to aid radiologists in detecting mammographic lesions which may indicate the presence of breast cancer. This study presents an automatic CADe system that uses local and discrete texture features for mammographic mass detection. This system segments some adaptive square regions of interest (ROIs) for suspicious areas. This study also proposes two complex feature extraction methods based on cooccurrence matrix and optical density transformation to describe local texture characteristics and the discrete photometric distribution of each ROI. Finally, this study uses stepwise linear discriminant analysis to classify abnormal regions by selecting and rating the individual performance of each feature. Results show that the proposed system achieves satisfactory detection performance.

Optimization of Network Topology in Computer-Aided Detection Schemes Using Phased Searching with NEAT in a Time-Scaled Framework.

In the field of computer-aided mammographic mass detection, many different features and classifiers have been tested. Frequently, the relevant features and optimal topology for the artificial neural network (ANN)-based approaches at the classification stage are unknown, and thus determined by trial-and-error experiments. In this study, we analyzed a classifier that evolves ANNs using genetic algorithms (GAs), which combines feature selection with the learning task. The classifier named “Phased Searching with NEAT in a Time-Scaled Framework” was analyzed using a dataset with 800 malignant and 800 normal tissue regions in a 10-fold cross-validation framework. The classification performance measured by the area under a receiver operating characteristic (ROC) curve was 0.856 ± 0.029. The result was also compared with four other well-established classifiers that include fixed-topology ANNs, support vector machines (SVMs), linear discriminant analysis (LDA), and bagged decision trees. The results show that Phased Searching outperformed the LDA and bagged decision tree classifiers, and was only significantly outperformed by SVM. Furthermore, the Phased Searching method required fewer features and discarded superfluous structure or topology, thus incurring a lower feature computational and training and validation time requirement. Analyses performed on the network complexities evolved by Phased Searching indicate that it can evolve optimal network topologies based on its complexification and simplification parameter selection process. From the results, the study also concluded that the three classifiers – SVM, fixed-topology ANN, and Phased Searching with NeuroEvolution of Augmenting Topologies (NEAT) in a Time-Scaled Framework – are performing comparably well in our mammographic mass detection scheme.