Masses In Mammograms Research Articles

Differentiation of Benign and Malignant Masses in Mammogram Using 2D-Fourier–Bessel Intrinsic Band Functions and Improved Feature Space

Differentiation of Benign and Malignant Masses in Mammogram Using 2D-Fourier–Bessel Intrinsic Band Functions and Improved Feature Space

Detection of multiple abnormalities of breast cancer in mammograms using a deep dilated fully convolutional neural network

Breast cancer is one of the serious health issues which are quite frequent among women and the mortality rate due to it can be reduced if detected and diagnosed at the early stage. In this paper, a new generalized deep learning architecture, deep dilated fully convolutional neural network (DDFCNN), is designed to detect multiple abnormalities like mass, microcalcification, and architectural distortion in mammograms. The proposed DDFCNN architecture consists of a feature and a dilation module. In feature module, convolutional kernels of various sizes are used to capture information at different scales. Since there is no loss of resolution in the output of the dilation module, it renders more details which helps in localizing the anomaly properly. Several experiments are conducted on a widely used databases — DDSM and mini-MIAS where an accuracy of 95.33%, 91.42%, and 92.67% is measured for detection of mass, microcalcification, and architectural distortion, respectively with a FPs/I of 0.43, 0.50, and 0.46 for DDSM and similar assessment for mini-MIAS database are 96.56%, 89.63%, and 95.07% with a FPs/I of 0.29, 0.51, and 0.31. Moreover, the overall performance in detection of all the abnormalities in combination is 93.14% with 0.46 as FPs/I for DDSM whereas for mini-MIAS is 93.75% with 0.37 as FPs/I. The performance is further compared with the state-of-the-other methods where the proposed approach takes an edge.

Improved PAA algorithm for breast mass detection in mammograms

Mammography screening is instrumental in the early detection and diagnosis of breast cancer by identifying masses in mammograms. With the rapid development of deep learning, numerous deep learning-based object detection algorithms have been explored for mass detection studies. However, these methods often yield a high false positive rate per image (FPPI) while achieving a high true positive rate (TPR). To maintain a higher TPR while also ensuring lower FPPI, we improved the Probability Anchor Assignment (PAA) algorithm to enhance the detection capability for mammographic characteristics with our previous work. We considered three dimensions: the backbone network, feature fusion module, and dense detection heads. The final experiment showed the effectiveness of the proposed method, and the TPR/FPPI values of the final improved PAA algorithm were 0.96/0.56 on the INbreast datasets. Compared to other methods, our method stands distinguished with its effectiveness in addressing the imbalance between positive and negative classes in cases of single lesion detection.

Automatic Classification of Cancerous Masses in Digital Mammograms Using Curvelet Analysis and Hybrid Genetic Algorithm-Artificial Neural Network Model

Background: Mammography is the most fundamental and widely used method for detecting breast abnormalities. Distinguishing malignant from benign lesions requires extracting relevant information, which can be challenging and time-consuming for radiologists. Computer-aided diagnosis (CAD) techniques can serve as complementary diagnostic tools, assisting radiologists in the early detection and analysis of abnormalities in mammograms. Objectives: This study aimed to propose a CAD system for extracting significant features of abnormalities in breast mammograms using Curvelet transform and fractal analysis, and classifying breast tumors as malignant or benign based on the calculated features. Patients and Methods: In this study, an efficient feature extraction method was applied, utilizing Curvelet transform and fractal analysis, on a dataset comprising 113 abnormal images from the Mammographic Image Analysis Society (MIAS) database, which included 62 benign and 51 malignant cases. The method yielded 575 features, but due to potential irrelevance or redundancy, a multi-objective optimization (MOO) approach based on a genetic algorithm (GA) for an artificial neural network (ANN), named GA-MOO-ANN, was proposed to obtain and focus on an optimal and effective feature set. As a result of this approach, a set of 17 efficient features was extracted. The proposed algorithm was implemented in MATLAB 2014a, and the performance metrics were calculated using 6-fold cross-validation. Results: The experimental results demonstrated exceptional performance, with an accuracy (Acc) of 98.2%, specificity (Sp) of 100%, sensitivity (Se) of 96.8%, positive predictive value (PPV) of 100%, negative predictive value (NPV) of 96.2%, and an impressive area under the curve (AUC) of 0.98, providing comparable results to other recent methods. Conclusion: The current findings suggest that the proposed method could be a valuable tool for breast cancer diagnosis, potentially reducing the number of unnecessary breast biopsies. This method may lead to more efficient patient evaluation and earlier detection of breast tumors.

Attention-map augmentation for hypercomplex breast cancer classification

Breast cancer is the most widespread neoplasm among women and early detection of this disease is critical. Deep learning techniques have become of great interest to improve diagnostic performance. However, distinguishing between malignant and benign masses in whole mammograms poses a challenge, as they appear nearly identical to an untrained eye, and the region of interest (ROI) constitutes only a small fraction of the entire image. In this paper, we propose a framework, parameterized hypercomplex attention maps (PHAM), to overcome these problems. Specifically, we deploy an augmentation step based on computing attention maps. Then, the attention maps are used to condition the classification step by constructing a multi-dimensional input comprised of the original breast cancer image and the corresponding attention map. In this step, a parameterized hypercomplex neural network (PHNN) is employed to perform breast cancer classification. The framework offers two main advantages. First, attention maps provide critical information regarding the ROI and allow the neural model to concentrate on it. Second, the hypercomplex architecture has the ability to model local relations between input dimensions thanks to hypercomplex algebra rules, thus properly exploiting the information provided by the attention map. We demonstrate the efficacy of the proposed framework on both mammography images as well as histopathological ones. We surpass attention-based state-of-the-art networks and the real-valued counterpart of our approach. The code of our work is available at https://github.com/ispamm/AttentionBCS.

An X across the Chest: A Rare Case of a Criss-crossed Sternalis Muscle.

The present case study aims at drawing attention to a very rare presentation of the sternalis muscle noticed during routine dissection, and is intended to highlight the clinical significance and usefulness of this unique muscle in reconstructive surgeries, especially of the breast. Though many morphological variants of the muscle have been reported, we came across a unique bilateral sternalis muscle during routine dissection for undergraduate medical teaching, in an 80-year-old male cadaver. The muscle originates on both sides from the external oblique aponeurosis from the fleshy belly, and after becom-ing tendinous, converges in the midline to form a common tendon at the level of the sternal angle, and then splits again into two tendons which become continuous with the ipsilateral sternocleidomastoid. Notwithstanding the fact that the presence of a sternalis can be misdiagnosed as a wide range of anterior chest wall lesions and tumors, especially with misdiag-nosis of breast masses in routine mammograms, it has great use as a muscular flap for reconstructive surgeries of the anterior chest wall, head, neck and breast.

EU-Net: Enhanced U-shaped Network for Breast Mass Segmentation.

Segmentation of breast masses in digital mammograms is very challenging due to its complexity. The recent U-shaped encoder-decoder networks achieved remarkable performance in medical image segmentation. However, these networks have some limitations: a) The multi-scale context information is required to accurately segment mass but is not effectively extracted and utilized. b) The global context information is often ignored by the skip connection. To overcome these limitations and achieve better segmentation, we propose an Enhanced U-shaped Network (EU-Net). The proposed EU-Net comprises of 3 novel components: 1) dense-block, which is employed in the encoder and the decoder in place of convolutional layers to achieve the multi-scale features. 2) Multi-Scale Feature Extraction and Fusion, which is used in the junction between the encoder and the decoder for further extracting and fusing the multi-scale context information. 3) Skip Connection Reconstruction, which is inserted between the encoder and the decoder at each stage, to redesign the skip connection and emphasize the global context information. Extensive experimental results under different settings show that the proposed EU-Net achieves superior performances than the previous state-of-the-art segmentation models, and other existing approaches on IN-Breast and CBIS-DDSM mammogram datasets. The generalization ability of the proposed EU-Net is evidenced through cross-dataset and ternary dataset evaluation performance. In the ternary dataset evaluation, the model is trained and evaluated on the UDIAT breast ultrasound dataset without fine-tuning. The EU-Net achieves higher generalization performance in both evaluation experiments. These experiments collectively indicate the efficiency and high generalization ability of the proposed EU-Net.

Presegmenter Cascaded Framework for Mammogram Mass Segmentation.

Accurate segmentation of breast masses in mammogram images is essential for early cancer diagnosis and treatment planning. Several deep learning (DL) models have been proposed for whole mammogram segmentation and mass patch/crop segmentation. However, current DL models for breast mammogram mass segmentation face several limitations, including false positives (FPs), false negatives (FNs), and challenges with the end-to-end approach. This paper presents a novel two-stage end-to-end cascaded breast mass segmentation framework that incorporates a saliency map of potential mass regions to guide the DL models for breast mass segmentation. The first-stage segmentation model of the cascade framework is used to generate a saliency map to establish a coarse region of interest (ROI), effectively narrowing the focus to probable mass regions. The proposed presegmenter attention (PSA) blocks are introduced in the second-stage segmentation model to enable dynamic adaptation to the most informative regions within the mammogram images based on the generated saliency map. Comparative analysis of the Attention U-net model with and without the cascade framework is provided in terms of dice scores, precision, recall, FP rates (FPRs), and FN outcomes. Experimental results consistently demonstrate enhanced breast mass segmentation performance by the proposed cascade framework across all three datasets: INbreast, CSAW-S, and DMID. The cascade framework shows superior segmentation performance by improving the dice score by about 6% for the INbreast dataset, 3% for the CSAW-S dataset, and 2% for the DMID dataset. Similarly, the FN outcomes were reduced by 10% for the INbreast dataset, 19% for the CSAW-S dataset, and 4% for the DMID dataset. Moreover, the proposed cascade framework's performance is validated with varying state-of-the-art segmentation models such as DeepLabV3+ and Swin transformer U-net. The presegmenter cascade framework has the potential to improve segmentation performance and mitigate FNs when integrated with any medical image segmentation framework, irrespective of the choice of the model.

Evaluation of Normalization Algorithms for Breast Mammogram Mass Segmentation

Medical images acquired at different institutions have different data distributions due to varying scanners, imaging protocols, or patient cohorts. Thus, normalizing samples before using them to train the Deep Learning (DL) models is an essential task. Scholars have used various normalization methods to normalize mammograms. However, which normalization method works best on mammograms and if it can help training generalize the DL model has not been analyzed in the literature. This paper presents a comparative analysis of commonly used normalization methods, such as scaling, Contrast Limited Adaptive Histogram Equalization (CLAHE), and Z-score normalization on mammograms for breast mass segmentation tasks. We trained the Attention Unet model on INbreast and the subset of the Cohort of Screen-Aged Women dataset (CSAW-S). We performed cross-dataset analysis on trained models to investigate how normalization methods helped train robust models to handle diverse datasets. Our results suggest that the normalizing method of input data affects the segmentation model’s performance. We achieved the best scores with the Z-score normalization closely followed by CLAHE. However, further investigation suggests that the normalization method’s performance may depend on the variability of mammograms in the dataset. Results of cross-dataset analysis suggest the importance of choosing the normalization method for consistent DL model performance. To our knowledge, this study represents the first systematic analysis of the performance of prevalent normalization techniques on a diverse dataset of mammogram images. The insights gained from our experiments can guide the selection of normalization methods for improved mass segmentation accuracy.

Mammogram mass segmentation and classification based on cross-view VAE and spatial hidden factor disentanglement.

Breast masses are the most important clinical findings of breast carcinomas. The mass segmentation and classification in mammograms remain a crucial yet challenging topic in computer-aided diagnosis systems, as the masses show their irregularities in shape, size and texture. In this paper, we propose a new framework for mammogram mass classification and segmentation. Specifically, to utilize the complementary information within the mammographic cross-views, cranio caudal and mediolateral oblique, a cross-view based variational autoencoder (CV-VAE) combined with a spatial hidden factor disentanglement module is presented, where the two views can be reconstructed from each other through two explicitly disentangled hidden factors: class related (specified) and background common (unspecified). Then, the specified factor is not only divided into two categories: benign and malignant by a new introduced feature pyramid networks based mass classifier, but also used to predict the mass mask label based on a U-Net-like decoder. By integrating the two complementary modules, more discriminative morphological and semantic features can be learned to solve the mass classification and segmentation problems simultaneously. The proposed method is evaluated on two most used public mammography datasets, CBIS-DDSM and INbreast, achieving the Dice similarity coefficient (DSC) of 92.46% and 93.70% for segmentation and the area under receiver operating characteristic curve (AUC) of 93.20% and 95.01% for classification, respectively. Compared with other state-of-the-art approaches, it gives competitive results.

Breast cancer diagnosis based on hybrid SqueezeNet and improved chef-based optimizer

The most frequent disease in women and the one that accounts for the majority of cancer-related fatalities in females is breast cancer. An important milestone in breast cancer CAD systems is the automatic recognition and delineation of masses in mammograms. We have developed a novel technique in this study to identify potential mass candidates in mammograms automatically. The suggested method is a pipeline method with three stages. First, a noise reduction method based on median filtering is performed to eliminate the noises. Then, the region of interest is threshold out of the pre-processed images using the Kapur technique. Afterward, to eliminate useless features, optimum feature extraction and selection are designed. It is established by an Improved version of the Chef-Based Optimization (ICBO) algorithm. Lastly, a classification is implemented based on an optimal SqueezeNet model for the final diagnosis. This classifier is also optimized based on the ICBO algorithm to maximize efficiency. Simulations of the method are applied to the MIAS database, and its results are compared with different state-of-the-art methods to show the method's advantage.

A Review Paper on Brief Study on Breast Cancer Classification using Deep Learning

The biggest cause of cancer-related deaths among women is breast cancer. The greatest and most efficient method to slow the growth of a tumour is early detection and diagnosis. The currently advised imaging technique for the early detection and diagnosis of breast cancer is mammography. Mammogram mass classifications continue to be a major difficulty and are essential for helping radiologists make an accurate diagnosis. The precise detection and classification of breast cancer is a crucial task in medical imaging because of the complexity of breast tissues. Due to their ability to automatically extract characteristics, deep learning techniques have been successfully applied in a wide range of industries, but particularly in the field of medical imaging. In order to identify and classify breast cancer on histopathology images, this research suggests an unique patch-based deep learning technique called Pa-DBN-BC (DBN). To extract features, unsupervised pre-training and supervised fine-tuning phases are performed. The network automatically extracts features from picture patches. To categorise the patches from histopathology images, logistic regression is performed. The model receives the characteristics extracted from the patches as input, and it outputs a probability matrix with either a positive sample (cancer) or a negative sample (background).

Hyperparameter Tuned Deep Hybrid Denoising Autoencoder Breast Cancer Classification on Digital Mammograms

Breast Cancer (BC) is considered the most commonly scrutinized cancer in women worldwide, affecting one in eight women in a lifetime. Mammography screening becomes one such standard method that is helpful in identifying suspicious masses’ malignancy of BC at an initial level. However, the prior identification of masses in mammograms was still challenging for extremely dense and dense breast categories and needs an effective and automatic mechanisms for helping radiotherapists in diagnosis. Deep learning (DL) techniques were broadly utilized for medical imaging applications, particularly breast mass classification. The advancements in the DL field paved the way for highly intellectual and self-reliant computer-aided diagnosis (CAD) systems since the learning capability of Machine Learning (ML) techniques was constantly improving. This paper presents a new Hyperparameter Tuned Deep Hybrid Denoising Autoencoder Breast Cancer Classification (HTDHDAE-BCC) on Digital Mammograms. The presented HTDHDAE-BCC model examines the mammogram images for the identification of BC. In the HTDHDAE-BCC model, the initial stage of image preprocessing is carried out using an average median filter. In addition, the deep convolutional neural network-based Inception v4 model is employed to generate feature vectors. The parameter tuning process uses the binary spider monkey optimization (BSMO) algorithm. The HTDHDAE-BCC model exploits chameleon swarm optimization (CSO) with the DHDAE model for BC classification. The experimental analysis of the HTDHDAE-BCC model is performed using the MIAS database. The experimental outcomes demonstrate the betterments of the HTDHDAE-BCC model over other recent approaches.

Combining modified hyper learning binary dragonfly algorithm and deep learning for BI‐RADS classification of breast masses in mammograms

AbstractBackgroundIt has always been difficult and challenging to quantify the breast imaging reporting and data system (BI‐RADS) criteria into several categories. Automatic quantitation can assist clinicians in the early diagnosis and treatment eventually reducing the mortality rate. As a result, in the recent years, early BC diagnosis methods based on pathological breast images have been in high demand.MethodWe propose a computer‐aided diagnosis (CAD) system that combines the transfer learning approach with meta‐heuristic optimization, and machine learning to classify BI‐RADS breast masses categories within levels 3 and 4. Transfer learning technique ResNet‐18 is used for high‐level feature extraction. The clinically important features are then chosen using a modified feature selection technique based on the Hyper Learning Binary DragonFly Algorithm (M‐HLBDA). Finally, a Fine K‐nearest neighbour (KNN) is employed for classification.ResultA series of mammography breast mass images from the curated breast imaging subset of DDSM (CBIS‐DDSM) are evaluated in order to categorize within BI‐RADS levels 3 and 4. Experimental findings demonstrated M‐HLBDA capability to identify the optimal feature subset, which minimizes the number of selected features and maximizes the classification. Our system attained classification accuracy of 87.5%, Sensitivity of 88.8%, Specificity of 86.5%, and AUC of 0.82 using KNN on selected features using M‐HLBDA.ConclusionOur model can annotate and classify BI‐RADS levels 3 and 4 with better classification accuracy, and it may be used as an automated system to help radiologists.

Feature selection and classification in mammography using hybrid crow search algorithm with Harris hawks optimization

The purpose of this study is to develop a hybrid algorithm for feature selection and classification of masses in digital mammograms based on the Crow search algorithm (CSA) and Harris hawks optimization (HHO). The proposed CSAHHO algorithm finds the best features depending on their fitness value, which is determined by an artificial neural network. Using an artificial neural network and support vector machine classifiers, the best features determined by CSAHHO are utilized to classify masses in mammograms as benign or malignant. The performance of the suggested method is assessed using 651 mammograms. Experimental findings show that the proposed CSAHHO tends to be the best as compared to the original CSA and HHO algorithms when evaluated using ANN. It achieves an accuracy of 97.85% with a kappa value of 0.9569 and area under curve AZ = 0.982 ± 0.006. Furthermore, benchmark datasets are used to test the feasibility of the suggested approach and then compared with four state-of-the-art algorithms. The findings indicate that CSAHHO achieves high performance with the least amount of features and support to enhance breast cancer diagnosis.

Segmentation of mammogram images using level set with Cuckoo search optimisation

ABSTRACT Breast cancer is alone accounted for 30% of all new cancer diagnoses for women. Early identification of breast cancer is a key stage in the process of diagnosis and treatment. Segmenting of low contrast mammograms is not an easy task due to irregular mass form, speculated margins and variation in intensity levels. Most of the region-based technique works well for homogenous images but fails for intensity inhomogeneity. Multiphase level set segmentation is optimised by the cuckoo search approach for energy minimisation. The proposed multiphase level set with the cuckoo optimisation method performs well in segmenting homogeneous as well as inhomogeneous regions. The Chan Vese (CV) and the proposed method are applied to the Mammographic Image Analysis Society (MIAS) database to obtain the accurate contour of the masses. To validate the proposed approach using quantitative performance measures such as Jaccard coefficient (JC), dice coefficient (DC), Hausdorff distance (Hd), sensitivity, specificity and accuracy were used for the MIAS data set. The results indicate that the suggested method can successfully segment the breast mass in mammograms and more robust than the classical CV method.

YOLO-LOGO: A transformer-based YOLO segmentation model for breast mass detection and segmentation in digital mammograms

Background and objectiveBoth mass detection and segmentation in digital mammograms play a crucial role in early breast cancer detection and treatment. Furthermore, clinical experience has shown that they are the upstream tasks of pathological classification of breast lesions. Recent advancements in deep learning have made the analyses faster and more accurate. This study aims to develop a deep learning model architecture for breast cancer mass detection and segmentation using the mammography. MethodsIn this work we proposed a double shot model for mass detection and segmentation simultaneously using a combination of YOLO (You Only Look Once) and LOGO (Local-Global) architectures. Firstly, we adopted YoloV5L6, the state-of-the-art object detection model, to position and crop the breast mass in mammograms with a high resolution; Secondly, to balance training efficiency and segmentation performance, we modified the LOGO training strategy to train the whole images and cropped images on the global and local transformer branches separately. The two branches were then merged to form the final segmentation decision. ResultsThe proposed YOLO-LOGO model was tested on two independent mammography datasets (CBIS-DDSM and INBreast). The proposed model performs significantly better than previous works. It achieves true positive rate 95.7% and mean average precision 65.0% for mass detection on CBIS-DDSM dataset. Its performance for mass segmentation on CBIS-DDSM dataset is F1-score=74.5% and IoU=64.0%. The similar performance trend is observed in another independent dataset INBreast as well. ConclusionsThe proposed model has a higher efficiency and better performance, reduces computational requirements, and improves the versatility and accuracy of computer-aided breast cancer diagnosis. Hence it has the potential to enable more assistance for doctors in early breast cancer detection and treatment, thereby reducing mortality.

Machine learning based computer aided diagnosis system for classification of breast masses in mammograms

Breast cancer continues to be the most common cancer in the fastest developing and the developed nations. Early detection by using mammography has been proven as the best prognosis. Computer Aided Diagnosis (CAD) systems are being used as second reader for the analysis and interpretation of mammogram images. In the last two decades, although breast cancer incidence has increased by many folds but unfortunately the progress in this field has almost stagnated. Therefore, the CAD systems need to be improved to be considered useful. In this study, a machine learning based CAD system for segmentation and classification of breast masses have been proposed. The IRMA Version of DDSM dataset has been used for experimentation and evaluation of the proposed system. Exact breast masses were segmented from manually extracted ROIs of 700*700 pixels by employing an improved seeded region growing algorithm. Various geometry and texture features were computed from the segmented mass lesions and corresponding ROIs respectively. The classification performances of nine state-of-the-art classifiers namely K-Nearest Neighbour (KNN), Support Vector Machine (SVM), Gaussian Mixture Model (GMM), Multi-class Support Vector Machine (mSVM), Decision Tree (DT), Discriminate Analysis (DA), Naive Bayes (NB), Random Forest (RF), Ensemble Tree (ET) have been investigated in this study. On evaluating the experimental results for all the classifiers, highest classification accuracy is obtained with SVM classifier. The experimental results reveal that the proposed improved seeded region growing approach has been proven helpful in improving the classification performance of the proposed system.

Breast cancer: Classification of suspicious regions in digital mammograms based on capsule network

Mammography screening is one of the common techniques that help identify suspicious masses' malignancy of breast cancer at an early stage. Yet, the early diagnosis of masses in mammograms is still challenging for dense and extremely dense breast categories and requires efficient and automated systems to assist radiologists in their diagnosis. Deep learning methods have widely been used for medical imaging applications, especially for breast masses classification. A novel capsule concept has recently been proposed to solve the drawbacks of deep learning models based on classic convolutional networks by learning the hierarchical structures in images. However, the originally proposed capsule network suffers from issues due to high number of parameters and high computational time. This paper proposes a new architecture of the capsule network that significantly reduces the computational time of the original capsule network by 6.5 times and makes the training of breast masses ROIs on affordable GPUs possible. The proposed architecture was fine-tuned according to the number of kernels and capsules and by using data augmentation. The results of the evaluation of the four breast density categories show that our capsule-based model outperforms existing techniques in classifying suspicious breast masses in one stage. The binary classification of masses into normal and abnormal achieves an accuracy of 96.03%, a F1-score of 96.27%, a precision of 94.28%, a recall of 98.38%, a specificity of 93.97%, and an AUC of 0.997. The multi-classification of breast masses into normal, benign, and malignant scores an accuracy of 77.78%, a F1-score of 77.45%, a precision of 71.54%, a recall of 84.54%, a specificity of 83.15%, and an AUC of 0.9.

Comparative analysis of proficiencies of various textures and geometric features in breast mass classification using k-nearest neighbor

This paper introduces a comparative analysis of the proficiencies of various textures and geometric features in the diagnosis of breast masses on mammograms. An improved machine learning-based framework was developed for this study. The proposed system was tested using 106 full field digital mammography images from the INbreast dataset, containing a total of 115 breast mass lesions. The proficiencies of individual and various combinations of computed textures and geometric features were investigated by evaluating their contributions towards attaining higher classification accuracies. Four state-of-the-art filter-based feature selection algorithms (Relief-F, Pearson correlation coefficient, neighborhood component analysis, and term variance) were employed to select the top 20 most discriminative features. The Relief-F algorithm outperformed other feature selection algorithms in terms of classification results by reporting 85.2% accuracy, 82.0% sensitivity, and 88.0% specificity. A set of nine most discriminative features were then selected, out of the earlier mentioned 20 features obtained using Relief-F, as a result of further simulations. The classification performances of six state-of-the-art machine learning classifiers, namely k-nearest neighbor (k-NN), support vector machine, decision tree, Naive Bayes, random forest, and ensemble tree, were investigated, and the obtained results revealed that the best classification results (accuracy = 90.4%, sensitivity = 92.0%, specificity = 88.0%) were obtained for the k-NN classifier with the number of neighbors having k = 5 and squared inverse distance weight. The key findings include the identification of the nine most discriminative features, that is, FD26 (Fourier Descriptor), Euler number, solidity, mean, FD14, FD13, periodicity, skewness, and contrast out of a pool of 125 texture and geometric features. The proposed results revealed that the selected nine features can be used for the classification of breast masses in mammograms.