scholarly journals An enhancement of mammogram images for breast cancer classification using artificial neural networks

2021 ◽  
Vol 10 (2) ◽  
pp. 332
Author(s):  
Jalpa J. Patel ◽  
S. K. Hadia
Keyword(s):  
Breast Cancer ◽  
Neural Networks ◽  
Area Under The Curve ◽  
The Neural Network ◽  
Analysis Society ◽  
Matthew’S Correlation Coefficient ◽  
Aided Diagnosis ◽  
Mammogram Images ◽  
Selection Of

<p><span id="docs-internal-guid-12eaaa5f-7fff-c428-95bf-97a7381b2976"><span>Breast cancer is the most driving reason for death in women in both developed and developing nations. For the plan of effective classification of a system, the selection of features method must be used to decrease irregularity part in mammogram images. The proposed approach is used to crop the region of interests (ROIs) manually. Based on that number of features are extracted. In this proposed method a novel hybrid optimum feature selection (HOFS) method is used to find out the significant features to reach maximum accuracy for this classification. A number of selected features is applied to train the neural network. In this proposed method accessible informational index from the mini–mammographic image analysis society (MIAS) database was used. The classification of this mammogram database involved a neural networks classifier which attained an accuracy of 99.7% with a sensitivity of 99.5%, and specificity of 100% as the area under the curve (AUC) is 0.9975 and matthew’s correlation coefficient (MCC) represents a binary class value which reached the value of 0.9931. It can be useful in a computer-aided diagnosis system (CAD) framework to help the radiologist in analyzing breast cancer. Results achieved with the proposed method are better compared to recent work.</span></span></p>

COMPUTER AIDED DIAGNOSIS SYSTEM FOR AUTOMATIC TWO STAGES CLASSIFICATION OF BREAST MASS IN DIGITAL MAMMOGRAM IMAGES

2019 ◽  
Vol 31 (01) ◽  
pp. 1950007 ◽  
Author(s):  
Ali Mohammad Alqudah ◽  
Huda M. S. Algharib ◽  
Amal M. S. Algharib ◽  
Hanan M. S. Algharib
Keyword(s):  
Breast Cancer ◽  
Computer Aided Diagnosis ◽  
Breast Mass ◽  
Breast Masses ◽  
Cad System ◽  
Computer Aided ◽  
Two Stages ◽  
Aided Diagnosis ◽  
Mammogram Images

Breast cancer is the most frequent cancer type that is diagnosed in women. The exact causes of such cancer are still unknown. Early and precise detection of breast cancer using mammogram images or biopsy to provide the required medications can increase the healing percentage. There are much current research efforts to developed a computer aided diagnosis (CAD) system based on mammogram images for detecting and classification of breast masses. In this research, a CAD system is developed for automated segmentation and two-stages classification of breast masses. The first stage includes the classification of the masses into seven classes (normal, calcification, circumscribed, spiculated, ill-defined, architectural distortion, asymmetry), which is done using probabilistic neural network (PNN). The second classification stage is to define the severity of abnormality into two classes (Benign and Malignant) which were done using support vector machine (SVM). The results of applying the proposed method on two mammogram image show that the accuracy of detection and segmentation of the breast mass was 99.8% for mammographic image analysis society database (MIAS-DB) with 322 images and 97.5% for breast cancer digital repository (BCDR), BCDR-F03 and BCDR-DN01 with 936 images, while for the first classification stage has accuracy of 97.08%, sensitivity of 98.30% and specificity of 89.8%, and the second classification stage has an accuracy of 99.18%, sensitivity of 98.42% and specificity of 94.90%.

CLASSIFICATION OF MAMMOGRAMS HAVING ARCHITECTURAL DISTORTION AND SPICULATED MASSES USING TEXTURE FEATURES

2021 ◽  
pp. 3-5
Author(s):  
D.B. Aghor ◽  
M.R. Banwaskar
Keyword(s):  
Breast Cancer ◽  
Texture Features ◽  
Screening Mammography ◽  
Support Vector ◽  
Architectural Distortion ◽  
Nonpalpable Breast Cancer ◽  
Current Systems ◽  
Aided Diagnosis ◽  
Mammogram Images

Architectural distortion is the third most common mammographic appearance of nonpalpable breast cancer, representing nearly 6% of abnormalities detected on screening mammography. Although its prevalence on mammography is small compared with calcication or visible mass, architectural distortion is also more difcult to diagnose because it can be subtle and variable in presentation. Early detection of breast cancer is possible by nding architectural distortion in monographic images. Spiculated masses account for about 14% of biopsied lesions and about 81% of these are malignant. Current CAD systems are dramatically better at detecting microcalcications than masses. The sensitivity is considerably lower for Spiculated Masses that are rated as "subtle" by radiologists Moreover, since current systems were devised with masses and calcications in mind, they don’t perform as well on other, less prevalent but still clinically signicant lesion types. In this paper, we propose a computer aided diagnosis system for distinguishing abnormal mammograms with architectural distortion or spiculated masses from normal mammograms. Five types of texture features GLCM, GLRLM, fractal texture, spectral texture and HOG features for the regions of suspicion are extracted. Support vector machine has been used as classier in this work. The proposed system yielded an overall accuracy of 97.29% for mammogram images collected from mini-MIAS database which is better as compared to existing methods.

scholarly journals A Research on Detection and Classification of Breast Cancer using k- means GMM & CNN Algorithms

2019 ◽  
Vol 8 (6S) ◽  
pp. 501-505
Keyword(s):  
Breast Cancer ◽  
Texture Feature ◽  
Region Of Interest ◽  
Medical Application ◽  
Second Step ◽  
Selection Algorithm ◽  
The Neural Network ◽  
Analysis Society ◽  
Medical Advances

Breast cancer, is a type of cancer that affects women in larger number in the world. Medical advances on all fronts to improve the care of patients and defeat this disease of the century. Because of this, it is essential that several disciplines continue to make their contribution and particularly data mining or artificial Intelligence. The classification of breast cancer is a medical application that poses a great challenge for researchers and scientists. Recently, the neural network has become a popular tool in the classification of cancer datasets. The proposed method consists of three steps: The first step is to find region of interest (ROI). The second step is texture feature extraction of ROI and optimization of features using optimized feature selection algorithm.. The third step is classification of detected abnormality as benign or malignant using Convolutional Neural Networks (CNN). The proposed method was evaluated using Mammographic Image Analysis Society MIAS) dataset. The proposed method has achieved 95.8% accuracy

Breast Cancer Detection and Classification using Traditional Computer Vision Techniques: A Comprehensive Review

2020 ◽  
Vol 16 ◽  
Author(s):  
Saliha Zahoor ◽  
Ikram Ullah Lali ◽  
Muhammad Attique Khan ◽  
Kashif Javed ◽  
Waqar Mehmood
Keyword(s):  
Breast Cancer ◽  
Computer Aided Diagnosis ◽  
Diagnose Breast Cancer ◽  
Initial Stage ◽  
Computer Aided ◽  
Diagnosis And Classification ◽  
And Performance ◽  
Tools And Techniques ◽  
Aided Diagnosis

: Breast Cancer is a common dangerous disease for women. In the world, many women died due to Breast cancer. However, in the initial stage, the diagnosis of breast cancer can save women's life. To diagnose cancer in the breast tissues there are several techniques and methods. The image processing, machine learning and deep learning methods and techniques are presented in this paper to diagnose the breast cancer. This work will be helpful to adopt better choices and reliable methods to diagnose breast cancer in an initial stage to survive the women's life. To detect the breast masses, microcalcifications, malignant cells the different techniques are used in the Computer-Aided Diagnosis (CAD) systems phases like preprocessing, segmentation, feature extraction, and classification. We have been reported a detailed analysis of different techniques or methods with their usage and performance measurement. From the reported results, it is concluded that for the survival of women’s life it is essential to improve the methods or techniques to diagnose breast cancer at an initial stage by improving the results of the Computer-Aided Diagnosis systems. Furthermore, segmentation and classification phases are challenging for researchers for the diagnosis of breast cancer accurately. Therefore, more advanced tools and techniques are still essential for the accurate diagnosis and classification of breast cancer.

Classification of Alloys with an Artificial Neural Network and Multivariate Calibration of Glow-Discharge Emission Spectra

1991 ◽  
Vol 45 (10) ◽  
pp. 1706-1716 ◽  
Author(s):  
Mark Glick ◽  
Gary M. Hieftje
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Glow Discharge ◽  
Multivariate Calibration ◽  
Emission Spectra ◽  
K Nearest Neighbor ◽  
The Neural Network ◽  
Calibration Sets ◽  
Artificial Neural

Artificial neural networks were constructed for the classification of metal alloys based on their elemental constituents. Glow discharge-atomic emission spectra obtained with a photodiode array spectrometer were used in multivariate calibrations for 7 elements in 37 Ni-based alloys (different types) and 15 Fe-based alloys. Subsets of the two major classes formed calibration sets for stepwise multiple linear regression. The remaining samples were used to validate the calibration models. Reference data from the calibration sets were then pooled into a single set to train neural networks with different architectures and different training parameters. After the neural networks learned to discriminate correctly among alloy classes in the training set, their ability to classify samples in the testing set was measured. In general, the neural network approach performed slightly better than the K-nearest neighbor method, but it suffered from a hidden classification mechanism and nonunique solutions. The neural network methodology is discussed and compared with conventional sample-classification techniques, and multivariate calibration of glow discharge spectra is compared with conventional univariate calibration.

scholarly journals Segmentation and Classification of Breast Cancer Tumour

2018 ◽  
Vol 2 (1) ◽  
pp. 14-18
Author(s):  
Gokalp Cinarer ◽  
Bulent Gursel Emiroglu ◽  
Ahmet Hasim Yurttakal
Keyword(s):  
Breast Cancer ◽  
Texture Features ◽  
Computer Aided Diagnosis ◽  
Wavelet Approximation ◽  
Proposed Model ◽  
Svm Algorithm ◽  
Computer Aided ◽  
Magnetic Resonance Imaging Mri ◽  
Aided Diagnosis

Breast cancer is cancer that forms in the cells of the breasts. Breast cancer is the most common cancer diagnosed in women in the world. Breast cancer can occur in both men and women, but it's far more common in women. Early detection of breast cancer tumours is crucial in the treatment. In this study, we presented a computer aided diagnosis expectation maximization segmentation and co-occurrence texture features from wavelet approximation tumour image of each slice and evaluated the performance of SVM Algorithm. We tested the model on 50 patients, among them, 25 are benign and 25 malign. The 80% of the images are allocated for training and 20% of images reserved for testing. The proposed model classified 2 patients correctly with success rate of 80% in case of 5 Fold Cross-Validation  Keywords: Breast Cancer, Computer-Aided Diagnosis (CAD), Magnetic Resonance Imaging (MRI);

Effect of GLCM Texture Features on the Medio-Lateral Oblique (MLO) View of Digital Mammograms for Breast Cancer Detection

2020 ◽  
Vol 9 (2) ◽  
pp. 25-44
Author(s):  
Usha N. ◽  
Sriraam N. ◽  
Kavya N. ◽  
Bharathi Hiremath ◽  
Anupama K Pujar ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Classification Accuracy ◽  
Breast Imaging ◽  
Texture Features ◽  
Support Vector ◽  
Risk Of Death ◽  
Automated Method ◽  
Occurrence Matrix ◽  
Aided Diagnosis ◽  
Mammogram Images

Breast cancer is one among the most common cancers in women. The early detection of breast cancer reduces the risk of death. Mammograms are an efficient breast imaging technique for breast cancer screening. Computer aided diagnosis (CAD) systems reduce manual errors and helps radiologists to analyze the mammogram images. The mammogram images are typically in two views, cranial-caudal (CC) and medio lateral oblique (MLO) views. MLO contains pectoral muscles (chest muscles) at the upper right or left corner of the image. In this study, it was removed by using a semi-automated method. All the normal and abnormal images were filtered and enhanced to improve the quality. GLCM (Gray Level Co-occurrence Matrix) texture features were extracted and analyzed by changing the number of features in a feature set. Linear Support Vector Machine (LSVM) was used as classifier. The classification accuracy was improved as the number of features in GLCM feature set increases. Simulation results show an overall classification accuracy of 96.7% with 19 GLCM features using SVM classifiers.

An improved diagnosis technique for breast cancer using LCFS and TreeHiCARe classifier model

Sensor Review ◽  
2019 ◽  
Vol 39 (1) ◽  
pp. 107-120 ◽  
Author(s):  
Deepika Kishor Nagthane ◽  
Archana M. Rajurkar
Keyword(s):  
Breast Cancer ◽  
Feature Extraction ◽  
Feature Selection ◽  
Decision Tree ◽  
Association Rule ◽  
Content Type ◽  
Tree Classifier ◽  
Mammogram Images ◽  
Diagnosis Technique

PurposeOne of the main reasons for increase in mortality rate in woman is breast cancer. Accurate early detection of breast cancer seems to be the only solution for diagnosis. In the field of breast cancer research, many new computer-aided diagnosis systems have been developed to reduce the diagnostic test false positives because of the subtle appearance of breast cancer tissues. The purpose of this study is to develop the diagnosis technique for breast cancer using LCFS and TreeHiCARe classifier model.Design/methodology/approachThe proposed diagnosis methodology initiates with the pre-processing procedure. Subsequently, feature extraction is performed. In feature extraction, the image features which preserve the characteristics of the breast tissues are extracted. Consequently, feature selection is performed by the proposed least-mean-square (LMS)-Cuckoo search feature selection (LCFS) algorithm. The feature selection from the vast range of the features extracted from the images is performed with the help of the optimal cut point provided by the LCS algorithm. Then, the image transaction database table is developed using the keywords of the training images and feature vectors. The transaction resembles the itemset and the association rules are generated from the transaction representation based ona priorialgorithm with high conviction ratio and lift. After association rule generation, the proposed TreeHiCARe classifier model emanates in the diagnosis methodology. In TreeHICARe classifier, a new feature index is developed for the selection of a central feature for the decision tree centered on which the classification of images into normal or abnormal is performed.FindingsThe performance of the proposed method is validated over existing works using accuracy, sensitivity and specificity measures. The experimentation of proposed method on Mammographic Image Analysis Society database resulted in classification of normal and abnormal cancerous mammogram images with an accuracy of 0.8289, sensitivity of 0.9333 and specificity of 0.7273.Originality/valueThis paper proposes a new approach for the breast cancer diagnosis system by using mammogram images. The proposed method uses two new algorithms: LCFS and TreeHiCARe. LCFS is used to select optimal feature split points, and TreeHiCARe is the decision tree classifier model based on association rule agreements.

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