Automatic Detection Of Masses Research Articles

Automatic detection of breast masses using deep learning with YOLO approach

Automatic detection of breast masses using deep learning with YOLO approach

A learning-based approach to breast cancer screening using mammography images

<p>The current big challenge facing radiologists in healthcare is the automatic detection and classification of masses in breast mammogram images. In the last few years, many researchers have proposed various solutions to this problem. These solutions are effectively dependent and work on annotated breast image data. But these solutions fail when applied to unlabeled and non-annotated breast image data. Therefore, this paper provides the solution to this problem with the help of a neural network that considers any kind of unlabeled data for its procedure. In this solution, the algorithm automatically extracts tumors in images using a segmentation approach, and after that, the features of the tumor are extracted for further processing. This approach used a double thresholding-based segmentation technique to obtain a perfect location of the tumor region, which was not possible in existing techniques in the literature. The experimental results also show that the proposed algorithm provides better accuracy compared to the accuracy of existing algorithms in the literature.</p>

Optimized hyperbolic tangent function-based contrast-enhanced mammograms for breast mass detection

Breast cancer is a grave concern among women due to its high mortality rate in women as compared to that in men. Mass, an early symptom of breast cancer, is difficult to detect due to its subtle nature. Mammography, an effective and most preferred screening technique, generally uses low-contrast images where identification of some of the mass lesions from surrounding normal tissues is a difficult task even for skilled radiologists. Therefore, to alleviate the issue, this paper introduces an effective computer-aided detection scheme based on a novel contrast enhancement scheme for mammograms to automate the localization procedure of mass cases. Hyperbolic tangent function, a simple yet effective method, is introduced as an enhancement transfer function whose adjustable parameter is optimized by the Tunicate swarm algorithm, a nature-inspired optimization algorithm, via fitness function. To minimize the false positives generated after the detection, the median robust extended local binary pattern, a texture-based descriptor, is introduced, which possesses the ability to capture both micro- and macro-structure information from an image patch. The analysis includes four classifiers: Fisher linear discriminant analysis, K-nearest neighbor, support vector machine, and an ensemble classifier in the feature-based classification for false positive reduction (FPR). The proposed approach of automatic detection of masses when verified using two standard databases, mini-MIAS and Digital Database for Screening Mammography, comprised 68 and 550 mammograms, respectively, achieved sensitivities of 95.3% and 94.1% with 0.36 and 0.40 false positives per image after false positive reduction.

S18 Deep Learning and Digital Single-Operator Cholangioscopy: Automatic Detection and Classification of Malignant Biliary Masses in Patients With Indeterminate Biliary Strictures

S18 Deep Learning and Digital Single-Operator Cholangioscopy: Automatic Detection and Classification of Malignant Biliary Masses in Patients With Indeterminate Biliary Strictures

An Efficient Method for Automated Breast Mass Segmentation and Classification in Digital Mammograms

Background: Automatic detection and classification of breast masses in mammograms are still challenging tasks. Today, computer-aided diagnosis (CAD) systems are being developed to assist radiologists in interpreting mammograms. Objectives: This study aimed to provide a novel method for automatic segmentation and classification of masses in mammograms to help radiologists make an accurate diagnosis. Materials and Methods: For an efficient mass diagnosis in mammograms, we proposed an automatic scheme to perform both mass detection and classification. First, a combination of several image enhancement algorithms, including contrast-limited adaptive histogram equalization (CLAHE), guided imaging, and median filtering, was investigated to enhance the visual features of breast area and increase the accuracy of segmentation outcomes. Second, the density of discrete wavelet coefficient density (DDWCs), based on the quincunx lifting scheme (QLS), was proposed to find suspicious mass regions or regions of interest (ROIs). Finally, mass lesions that appeared in the mammogram were classified into four categories of benign, probably benign, malignant, and probably malignant, based on the morphological shape. The proposed method was evaluated among 1593 images from the Curated Breast Imaging Subset-Digital Database for Screening Mammography (CBIS-DDSM) dataset. Results: The experimental results revealed that the suspected region localization had 100% sensitivity, with a mean of 6.4 ± 4.5 false positive (FP) detections per image. Moreover, the results showed an overall accuracy of 85.9% and an area under the curve (AUC) of 0.901 for the mass classification algorithm. Conclusion: The present results showed the comparable performance of our proposed method to that of the state-of-the-art methods.

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.

A Review on Feature Selection Techniques in Digital Mammograms

The most of the women in the world are suffering from a deadly disease called Breast Cancer (BC). Breast cancer is analyzed by using imaging modalities such as mammograms, magnetic resonance imaging, ultrasound, and thermograms. Among all, mammograms are the low dosage, less cost, more effective, and accurate method to detect BC in early stages. There are many Computer-Aided Detection (CAD) systems for the automatic detection of masses in mammograms. These techniques are helping radiologists and physicians in diagnosing disease. The objective of this paper is to overview different CAD systems in which mainly we focused on feature selection, as feature selection techniques are used to reduce the complexity of the classifiers and also increase the accuracy. We conclude that suitable optimization techniques should be chosen to increase the accuracy of the classifier so that we can increase the survival rate of the patient.

A computer-aided approach for automatic detection of breast masses in digital mammogram via spectral clustering and support vector machine.

Breast cancer continues to be a widespread health concern all over the world. Mammography is an important method in the early detection of breast abnormalities. In recent years, using an automatic Computer-Aided Detection (CAD) system based on image processing techniques has been a more reliable interpretation in the illustration of breast distortion. In this study, a fully process-integrated approach with developing a CAD system is presented for the detection of breast masses based on texture description, spectral clustering, and Support Vector Machine (SVM). To this end, breast Regions of Interest (ROIs) are automatically detected from digital mammograms via gray-scale enhancement and data cleansing. The ROIs are segmented as labeled multi-sectional patterns using spectral clustering by the means of intensity descriptors relying on the region's histogram and texture descriptors based on the Gray Level Co-occurrence Matrix (GLCM). In the next step, shape and probabilistic features are derived from the segmented sections and given to the Genetic Algorithm (GA) to do the feature selection. The optimal feature vector comprising a fusion of selected shape and probabilistic features is submitted to linear kernel SVM for robust and reliable classification of mass tissues from the non-mass. Linear discrimination analysis (LDA) is also performed to ascertain the significance of the nominated feature space. The classification results of the proposed approach are presented by sensitivity, specificity, and accuracy measures, which are 89.5%, 91.2%, and 90%, respectively.

Automatic Detection of Masses From Mammographic Images via Artificial Intelligence Techniques

A novel computer-aided tool for automated sensing of normal tissue and abnormal masses from mammographic X-ray images is described. The pre-processing technique was firstly adopted for noise elimination on mammographic images. The automatic initialization of active contour was then placed on the pre-processed image for segmentation followed by deep convolutional neural networks to extract the features. Principal component analysis was then applied to choose the most significant features as input to the support vector machine classifier. Lastly, k-fold cross-validation techniques were executed for results validation. The developed tool was tested on public available datasets, namely Mammographic Image Analysis Society, and Digital Database for Screening Mammogram, based on eight evaluation methods: accuracy, sensitivity, specificity, receiver operating characteristic curve, area under curve (AUC), F1-score, precision, and recall. The outcome demonstrated the proposed system as a competitive tool in assisting radiologists as it attains an average of 95.24%, 93.94%, 96.61%, 94.66, 93.00%, 94.34%, and 0.98 for accuracy, sensitivity, specificity, precision, recall, F1-score, and AUC, respectively for testing on a combination of the aforementioned two datasets.

Automatic Detection of Masses in Mammograms Using Bi-Dimensional Empirical Mode Decomposition

Automatic Detection of Masses in Mammograms Using Bi-Dimensional Empirical Mode Decomposition

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.

Bootstrap-based procedures for inference in nonparametric receiver-operating characteristic curve regression analysis.

Prior to using a diagnostic test in a routine clinical setting, the rigorous evaluation of its diagnostic accuracy is essential. The receiver-operating characteristic curve is the measure of accuracy most widely used for continuous diagnostic tests. However, the possible impact of extra information about the patient (or even the environment) on diagnostic accuracy also needs to be assessed. In this paper, we focus on an estimator for the covariate-specific receiver-operating characteristic curve based on direct regression modelling and nonparametric smoothing techniques. This approach defines the class of generalised additive models for the receiver-operating characteristic curve. The main aim of the paper is to offer new inferential procedures for testing the effect of covariates on the conditional receiver-operating characteristic curve within the above-mentioned class. Specifically, two different bootstrap-based tests are suggested to check (a) the possible effect of continuous covariates on the receiver-operating characteristic curve and (b) the presence of factor-by-curve interaction terms. The validity of the proposed bootstrap-based procedures is supported by simulations. To facilitate the application of these new procedures in practice, an R-package, known as npROCRegression, is provided and briefly described. Finally, data derived from a computer-aided diagnostic system for the automatic detection of tumour masses in breast cancer is analysed.

Automatic Detection and Classification of Masses in Digital Mammograms

Automatic Detection and Classification of Masses in Digital Mammograms

Automatic Detection and Classification of Masses in Digital Mammograms

Automatic Detection and Classification of Masses in Digital Mammograms

Automatic Detection of Masses in Mammograms Using Quality Threshold Clustering, Correlogram Function, and SVM.

Breast cancer is the second most common type of cancer in the world. Several computer-aided detection and diagnosis systems have been used to assist health experts and to indicate suspect areas that would be difficult to perceive by the human eye; this approach has aided in the detection and diagnosis of cancer. The present work proposes a method for the automatic detection of masses in digital mammograms by using quality threshold (QT), a correlogram function, and the support vector machine (SVM). This methodology comprises the following steps: The first step is to perform preprocessing with a low-pass filter, which increases the scale of the contrast, and the next step is to use an enhancement to the wavelet transform with a linear function. After the preprocessing is segmentation using QT; then, we perform post-processing, which involves the selection of the best mass candidates. This step is performed by analyzing the shape descriptors through the SVM. For the stage that involves the extraction of texture features, we used Haralick descriptors and a correlogram function. In the classification stage, the SVM was again used for training, validation, and final test. The results were as follows: sensitivity 92.31 %, specificity 82.2 %, accuracy 83.53 %, mean rate of false positives per image 1.12, and area under the receiver operating characteristic (ROC) curve 0.8033. Breast cancer is notable for presenting the highest mortality rate in addition to one of the smallest survival rates after diagnosis. An early diagnosis means a considerable increase in the survival chance of the patients. The methodology proposed herein contributes to the early diagnosis and survival rate and, thus, proves to be a useful tool for specialists who attempt to anticipate the detection of masses.

Advanced spectral analyses for real-time automatic echographic tissue-typing of simulated tumor masses at different compression stages

Prototypal software algorithms for advanced spectral analysis of echographic images were developed to perform automatic detection of simulated tumor masses at two different pathological stages. Previously published works documented the possibility of characterizing macroscopic variation of mechanical properties of tissues through elastographic techniques, using different imaging modalities, including ultrasound (US); however, the accuracy of US-based elastography remains affected by the variable manual modality of the applied compression and several attempts are under investigation to overcome this limitation. Quantitative US (QUS), such as Fourier- and wavelet-based analyses of the RF signal associated with the US images, has been developed to perform a microscopic-scale tissue-type imaging offering new solutions for operator-independent examinations. Because materials able to reproduce the harmonic behavior of human liver can be realized, in this study, tissue-mimicking structures were US imaged and the related RF signals were analyzed using wavelet transform through an in-house-developed algorithm for tissue characterization. The classification performance and reliability of the procedure were evaluated on two different tumor stiffnesses (40 and 130 kPa) and with two different applied compression levels (0 and 3.5 N). Our results demonstrated that spectral components associated with different levels of tissue stiffness within the medium exist and can be mapped onto the original US images independently of the applied compressive forces. This wavelet-based analysis was able to identify different tissue stiffness with satisfactory average sensitivity and specificity: respectively, 72.01% ± 1.70% and 81.28% ± 2.02%.

Masses Detection in Digital Mammogram by Gray Level Reduction using Texture coding Method

Breast cancer is the most common cancer in women around the world. Various countries including the UAE offer asymptomatic screening for the disease. The interpretation of mammograms is a very challenges task and is subject to human error. Computeraided detection and diagnosis have been proposed as a second reader for helping radiologists perform this difficult task. Texture features have been widely used as classification of masses in digital mammogram. In this paper we proposed a method for automatic detection of masses in digital mammogram. The proposed method uses the coding technique achieved good accuracy with Linear Discriminant Analysis (LDA) classification. The classification accuracy by using the coded images is improved much compared to one that obtained from the original image.

Comparative study of ROC regression techniques—Applications for the computer-aided diagnostic system in breast cancer detection

The receiver operating characteristic (ROC) curve is the most widely used measure for statistically evaluating the discriminatory capacity of continuous biomarkers. It is well known that, in certain circumstances, the markers’ discriminatory capacity can be affected by factors, and several ROC regression methodologies have been proposed to incorporate covariates in the ROC framework. An in-depth simulation study of different ROC regression models and their application in the emerging field of automatic detection of tumour masses is presented. In the simulation study different scenarios were considered and the models were compared to each other on the basis of the mean squared error criterion. The application of the reviewed ROC regression techniques in evaluating computer-aided diagnostic (CAD) schemes can become a major factor in the development of such systems in Radiology.

A review of automatic mass detection and segmentation in mammographic images

The aim of this paper is to review existing approaches to the automatic detection and segmentation of masses in mammographic images, highlighting the key-points and main differences between the used strategies. The key objective is to point out the advantages and disadvantages of the various approaches. In contrast with other reviews which only describe and compare different approaches qualitatively, this review also provides a quantitative comparison. The performance of seven mass detection methods is compared using two different mammographic databases: a public digitised database and a local full-field digital database. The results are given in terms of Receiver Operating Characteristic (ROC) and Free-response Receiver Operating Characteristic (FROC) analysis.