scholarly journals Bayesian Classifier with Simplified Learning Phase for Detecting Microcalcifications in Digital Mammograms

2009 ◽  
Vol 2009 ◽  
pp. 1-13 ◽  
Author(s):  
Imad Zyout ◽  
Ikhlas Abdel-Qader ◽  
Christina Jacobs
Keyword(s):  
Breast Cancer ◽  
Decision Function ◽  
Bayesian Classifier ◽  
Learning Phase ◽  
True Positive ◽  
Statistical Features ◽  
Training Samples ◽  
Significant Step ◽  
Microcalcification Clusters ◽  
New Framework

Detection of clustered microcalcifications (MCs) in mammograms represents a significant step towards successful detection of breast cancer since their existence is one of the early signs of cancer. In this paper, a new framework that integrates Bayesian classifier and a pattern synthesizing scheme for detecting microcalcification clusters is proposed. This proposed work extracts textural, spectral, and statistical features of each input mammogram and generates models of real MCs to be used as training samples through a simplified learning phase of the Bayesian classifier. Followed by an estimation of the classifier's decision function parameters, a mammogram is segmented into the identified targets (MCs) against background (healthy tissue). The proposed algorithm has been tested using 23 mammograms from the mini-MIAS database. Experimental results achieved MCs detection with average true positive (sensitivity) and false positive (specificity) of 91.3% and 98.6%, respectively. Results also indicate that the modeling of the real MCs plays a significant role in the performance of the classifier and thus should be given further investigation.

scholarly journals Computer Vision-Based Microcalcification Detection in Digital Mammograms Using Fully Connected Depthwise Separable Convolutional Neural Network

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4854
Author(s):  
Khalil ur Rehman ◽  
Jianqiang Li ◽  
Yan Pei ◽  
Anaa Yasin ◽  
Saqib Ali ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Computer Vision ◽  
Clinical Work ◽  
True Positive Rate ◽  
Classification Performance ◽  
Color Contrast ◽  
True Positive ◽  
Positive Ratio ◽  
Microcalcification Clusters

Microcalcification clusters in mammograms are one of the major signs of breast cancer. However, the detection of microcalcifications from mammograms is a challenging task for radiologists due to their tiny size and scattered location inside a denser breast composition. Automatic CAD systems need to predict breast cancer at the early stages to support clinical work. The intercluster gap, noise between individual MCs, and individual object’s location can affect the classification performance, which may reduce the true-positive rate. In this study, we propose a computer-vision-based FC-DSCNN CAD system for the detection of microcalcification clusters from mammograms and classification into malignant and benign classes. The computer vision method automatically controls the noise and background color contrast and directly detects the MC object from mammograms, which increases the classification performance of the neural network. The breast cancer classification framework has four steps: image preprocessing and augmentation, RGB to grayscale channel transformation, microcalcification region segmentation, and MC ROI classification using FC-DSCNN to predict malignant and benign cases. The proposed method was evaluated on 3568 DDSM and 2885 PINUM mammogram images with automatic feature extraction, obtaining a score of 0.97 with a 2.35 and 0.99 true-positive ratio with 2.45 false positives per image, respectively. Experimental results demonstrated that the performance of the proposed method remains higher than the traditional and previous approaches.

scholarly journals Mammographic microcalcifications and risk of breast cancer

2021 ◽  
Author(s):  
Shadi Azam ◽  
Mikael Eriksson ◽  
Arvid Sjölander ◽  
Marike Gabrielson ◽  
Roxanna Hellgren ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Postmenopausal Women ◽  
Detection System ◽  
Premenopausal Women ◽  
Computer Aided Detection ◽  
Comprehensive Information ◽  
Estimated Risk ◽  
Logistic Regressions ◽  
Microcalcification Clusters

Abstract Background Mammographic microcalcifications are considered early signs of breast cancer (BC). We examined the association between microcalcification clusters and the risk of overall and subtype-specific BC. Furthermore, we studied how mammographic density (MD) influences the association between microcalcification clusters and BC risk. Methods We used a prospective cohort (n = 53,273) of Swedish women with comprehensive information on BC risk factors and mammograms. The total number of microcalcification clusters and MD were measured using a computer-aided detection system and the STRATUS method, respectively. Cox regressions and logistic regressions were used to analyse the data. Results Overall, 676 women were diagnosed with BC. Women with ≥3 microcalcification clusters had a hazard ratio [HR] of 2.17 (95% confidence interval [CI] = 1.57–3.01) compared to women with no clusters. The estimated risk was more pronounced in premenopausal women (HR = 2.93; 95% CI = 1.67–5.16). For postmenopausal women, microcalcification clusters and MD had a similar influence on BC risk. No interaction was observed between microcalcification clusters and MD. Microcalcification clusters were significantly associated with in situ breast cancer (odds ratio: 2.03; 95% CI = 1.13–3.63). Conclusions Microcalcification clusters are an independent risk factor for BC, with a higher estimated risk in premenopausal women. In postmenopausal women, microcalcification clusters have a similar association with BC as baseline MD.

Analysis of Microcalcifications Using Block Wise Local Binary Pattern and Independent component analysis in Mammograms

2019 ◽  
Vol 3 (1) ◽  
Author(s):  
Spandana Paramkusham ◽  
Dr. Kunda M.M. Rao ◽  
Dr. BVVSN Prabhakar Rao
Keyword(s):  
Breast Cancer ◽  
Independent Component Analysis ◽  
Local Binary Pattern ◽  
Texture Features ◽  
Component Analysis ◽  
Independent Component ◽  
Significant Shift ◽  
Statistical Features ◽  
Calcium Deposits

In India, the average age of developing a breast cancer has undergone a significant shift over last few decades. Most prominent features that indicate breast cancer are microcalcifications. Microcalcifications are tiny calcium deposits deposited on skin and non-palpable. Automatic analysis of microcalcification helps specialist in having more precise decision. The paper presents an approach that involves classification of microcalcifications into benign/malignant in mammograms. Texture features such LBP and statistical features are extracted from ROIs with microcalcification and independent component analysis is applied to reduce the feature set. These feature set is fed to artificial neural networks to classify the ROIs into malignant and benign calcifications.

scholarly journals Convolutional Neural Networks for the Segmentation of Microcalcification in Mammography Imaging

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Gabriele Valvano ◽  
Gianmarco Santini ◽  
Nicola Martini ◽  
Andrea Ripoli ◽  
Chiara Iacconi ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
False Positive ◽  
False Positive Rate ◽  
Novel Approach ◽  
Positive Rate ◽  
Microcalcification Detection ◽  
Microcalcification Clusters

Cluster of microcalcifications can be an early sign of breast cancer. In this paper, we propose a novel approach based on convolutional neural networks for the detection and segmentation of microcalcification clusters. In this work, we used 283 mammograms to train and validate our model, obtaining an accuracy of 99.99% on microcalcification detection and a false positive rate of 0.005%. Our results show how deep learning could be an effective tool to effectively support radiologists during mammograms examination.

scholarly journals A Weighted SVM-Based Approach to Tree Species Classification at Individual Tree Crown Level Using LiDAR Data

2019 ◽  
Vol 11 (24) ◽  
pp. 2948 ◽  
Author(s):  
Hoang Minh Nguyen ◽  
Begüm Demir ◽  
Michele Dalponte
Keyword(s):  
Tree Species ◽  
Remote Sensing Data ◽  
Classification Problem ◽  
Learning Phase ◽  
Support Vector ◽  
Lidar Data ◽  
Species Classification ◽  
Individual Tree ◽  
Tree Species Classification ◽  
Training Samples

Tree species classification at individual tree crowns (ITCs) level, using remote-sensing data, requires the availability of a sufficient number of reliable reference samples (i.e., training samples) to be used in the learning phase of the classifier. The classification performance of the tree species is mainly affected by two main issues: (i) an imbalanced distribution of the tree species classes, and (ii) the presence of unreliable samples due to field collection errors, coordinate misalignments, and ITCs delineation errors. To address these problems, in this paper, we present a weighted Support Vector Machine (wSVM)-based approach for the detection of tree species at ITC level. The proposed approach initially extracts (i) different weights associated to different classes of tree species, to mitigate the effect of the imbalanced distribution of the classes; and (ii) different weights associated to different training samples according to their importance for the classification problem, to reduce the effect of unreliable samples. Then, in order to exploit different weights in the learning phase of the classifier a wSVM algorithm is used. The features to characterize the tree species at ITC level are extracted from both the elevation and intensity of airborne light detection and ranging (LiDAR) data. Experimental results obtained on two study areas located in the Italian Alps show the effectiveness of the proposed approach.

scholarly journals Detection of potential microcalcification clusters using multivendor for‐presentation digital mammograms for short‐term breast cancer risk estimation

2019 ◽  
Vol 46 (4) ◽  
pp. 1938-1946 ◽  
Author(s):  
Maya Alsheh Ali ◽  
Mikael Eriksson ◽  
Kamila Czene ◽  
Per Hall ◽  
Keith Humphreys
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Risk Estimation ◽  
Short Term ◽  
Microcalcification Clusters

scholarly journals Analysis of Machine Learning Techniques Applied to the Classification of Masses and Microcalcification Clusters in Breast Cancer Computer-Aided Detection

2012 ◽  
Vol 03 (06) ◽  
pp. 1020-1028 ◽  
Author(s):  
Edén A. Alanís-Reyes ◽  
José L. Hernández-Cruz ◽  
Jesús S. Cepeda ◽  
Camila Castro ◽  
Hugo Terashima-Marín ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Machine Learning Techniques ◽  
Computer Aided Detection ◽  
Learning Techniques ◽  
Computer Aided ◽  
Microcalcification Clusters

scholarly journals Improving the Performance of Deep Learning Model-Based Classification by the Analysis of Local Probability

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Guanghao Jin ◽  
Yixin Hu ◽  
Yuming Jiao ◽  
Junfang Wen ◽  
Qingzeng Song
Keyword(s):  
Deep Learning ◽  
Local Environment ◽  
Learning Model ◽  
Human Beings ◽  
Training Samples ◽  
Local Probability ◽  
Validation Set ◽  
Deep Learning Model ◽  
New Framework ◽  
Better Than

Generally, the performance of deep learning-based classification models is highly related to the captured features of training samples. When a sample is not clear or contains a similar number of features of many objects, we cannot easily classify what it is. Actually, human beings classify objects by not only the features but also some information such as the probability of these objects in an environment. For example, when we know further information such as one object has a higher probability in the environment than the others, we can easily give the answer about what is in the sample. We call this kind of probability as local probability as this is related to the local environment. In this paper, we carried out a new framework that is named L-PDL to improve the performance of deep learning based on the analysis of this kind of local probability. Firstly, our method trains the deep learning model on the training set. Then, we can get the probability of objects on each sample by this trained model. Secondly, we get the posterior local probability of objects on the validation set. Finally, this probability conditionally cooperates with the probability of objects on testing samples. We select three popular deep learning models on three real datasets for the evaluation. The experimental results show that our method can obviously improve the performance on the real datasets, which is better than the state-of-the-art methods.

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