Classification Of Malignant Masses Research Articles

Enhancement Technique Based on the Breast Density Level for Mammogram for Computer-Aided Diagnosis.

Mass detection in mammograms has a limited approach to the presence of a mass in overlapping denser fibroglandular breast regions. In addition, various breast density levels could decrease the learning system's ability to extract sufficient feature descriptors and may result in lower accuracy performance. Therefore, this study is proposing a textural-based image enhancement technique named Spatial-based Breast Density Enhancement for Mass Detection (SbBDEM) to boost textural features of the overlapped mass region based on the breast density level. This approach determines the optimal exposure threshold of the images' lower contrast limit and optimizes the parameters by selecting the best intensity factor guided by the best Blind/Reference-less Image Spatial Quality Evaluator (BRISQUE) scores separately for both dense and non-dense breast classes prior to training. Meanwhile, a modified You Only Look Once v3 (YOLOv3) architecture is employed for mass detection by specifically assigning an extra number of higher-valued anchor boxes to the shallower detection head using the enhanced image. The experimental results show that the use of SbBDEM prior to training mass detection promotes superior performance with an increase in mean Average Precision (mAP) of 17.24% improvement over the non-enhanced trained image for mass detection, mass segmentation of 94.41% accuracy, and 96% accuracy for benign and malignant mass classification. Enhancing the mammogram images based on breast density is proven to increase the overall system's performance and can aid in an improved clinical diagnosis process.

An automatic breast computer‐aided diagnosis scheme based on a weighted fusion of relevant features and a deep CNN classifier

Mammography continues to play a central part in the early breast masses diagnosis and has raised several challenges in breast cancer detection. Nevertheless, it remains still difficult to detect abnormalities in a dense breast and some benign lesions may have similar appearance with masses. This study proposes a novel Computer-Aided Diagnosis (CADx) allowing benign and malignant mass classification. Accordingly, relevant textural-shape descriptors are proposed, which is the aggregation of a new Local Binary based feature, namely the Monogenic Gray Level and Local Difference (MGLLD), where both texture characteristics and breast tissue density information, and the Zernike moments are incorporated. A heuristic algorithm is then devised for optimizing the proposed features with respect to inter-class and intra-class distribution, by a convenient ponderation. Then, a set of classifiers are applied for opting to the best decision making solution. The Deep CNN yields the best accuracy, with an Area Under Curve (AUC) of 0.98 on Curated Breast Imaging Subset of DDSM (CBIS-DDSM). In addition, the authors are based on a subjective approval of the extracted Region Of Interest (ROI) by experts in radiology. The proposed scheme proves its effectiveness especially on some challenging breast cancer cases, corresponding to higher breast tissue density. In medical image processing, these results open new perspectives with respect to the use of the deep learning solutions, where the small sample-number is known to be a limitation (because pathologies cannot be intentionally provoked).

Diagnosis of breast tissue in mammography images based local feature descriptors

Breast cancer is one of the leading causes of death by cancer among women. The high mortality rates and the occurrence of this cancer worldwide show the importance of the investigation and development of means for the detection and early diagnosis of this disease. Computer-Aided Detection and Diagnosis systems have been developed to improve diagnostic accuracy by radiologists. This work proposes a method for discriminating patterns of malignancy and benignity of masses in digitized mammography images through the analysis of local features. The method comparatively applies the Scale-Invariant Feature Transform (SIFT), Speed Up Robust Feature (SURF), Oriented Fast and Rotated BRIEF (ORB) and Local Binary Pattern (LBP) descriptors for local feature extraction. These features are represented by a Bag of Features (BoF) model, applied to provide new representations of the data and to reduce its dimensionality. Finally, the features are used as input for the Support Vector Machine (SVM), Adaptive Boosting (Adaboost) and Random Forests (RF) classifiers to differentiate malignant and benign masses. The method obtained significant results, reaching 100% sensitivity, 99.65% accuracy and 99.24% specificity for benign and malignant mass classification.