scholarly journals Classification of Breast Masses Using a Computer-Aided Diagnosis Scheme of Contrast Enhanced Digital Mammograms

2018 ◽  
Vol 46 (9) ◽  
pp. 1419-1431 ◽  
Author(s):  
Gopichandh Danala ◽  
Bhavika Patel ◽  
Faranak Aghaei ◽  
Morteza Heidari ◽  
Jing Li ◽  
...  
Keyword(s):  
Computer Aided Diagnosis ◽  
Breast Masses ◽  
Contrast Enhanced ◽  
Computer Aided ◽  
Aided Diagnosis

scholarly journals A New Computer-Aided Diagnosis System with Modified Genetic Feature Selection for BI-RADS Classification of Breast Masses in Mammograms

2020 ◽  
Vol 2020 ◽  
pp. 1-17 ◽  
Author(s):  
Said Boumaraf ◽  
Xiabi Liu ◽  
Chokri Ferkous ◽  
Xiaohong Ma
Keyword(s):  
Feature Selection ◽  
Predictive Value ◽  
Breast Imaging ◽  
Feature Selection Method ◽  
Computer Aided Diagnosis ◽  
Screening Mammography ◽  
Breast Masses ◽  
Computer Aided ◽  
Aided Diagnosis

Mammography remains the most prevalent imaging tool for early breast cancer screening. The language used to describe abnormalities in mammographic reports is based on the Breast Imaging Reporting and Data System (BI-RADS). Assigning a correct BI-RADS category to each examined mammogram is a strenuous and challenging task for even experts. This paper proposes a new and effective computer-aided diagnosis (CAD) system to classify mammographic masses into four assessment categories in BI-RADS. The mass regions are first enhanced by means of histogram equalization and then semiautomatically segmented based on the region growing technique. A total of 130 handcrafted BI-RADS features are then extracted from the shape, margin, and density of each mass, together with the mass size and the patient’s age, as mentioned in BI-RADS mammography. Then, a modified feature selection method based on the genetic algorithm (GA) is proposed to select the most clinically significant BI-RADS features. Finally, a back-propagation neural network (BPN) is employed for classification, and its accuracy is used as the fitness in GA. A set of 500 mammogram images from the digital database for screening mammography (DDSM) is used for evaluation. Our system achieves classification accuracy, positive predictive value, negative predictive value, and Matthews correlation coefficient of 84.5%, 84.4%, 94.8%, and 79.3%, respectively. To our best knowledge, this is the best current result for BI-RADS classification of breast masses in mammography, which makes the proposed system promising to support radiologists for deciding proper patient management based on the automatically assigned BI-RADS categories.

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%.

Particle swarm optimized computer aided diagnosis system for classification of breast masses

2017 ◽  
Vol 32 (4) ◽  
pp. 2819-2828 ◽  
Author(s):  
Stephan Punitha ◽  
Subban Ravi ◽  
M. Anousouya Devi ◽  
Jothimani Vaishnavi
Keyword(s):  
Particle Swarm ◽  
Computer Aided Diagnosis ◽  
Breast Masses ◽  
Diagnosis System ◽  
Computer Aided ◽  
Aided Diagnosis

scholarly journals Computer-aided diagnosis for the classification of focal liver lesions by use of contrast-enhanced ultrasonography

2008 ◽  
Vol 35 (5) ◽  
pp. 1734-1746 ◽  
Author(s):  
Junji Shiraishi ◽  
Katsutoshi Sugimoto ◽  
Fuminori Moriyasu ◽  
Naohisa Kamiyama ◽  
Kunio Doi
Keyword(s):  
Computer Aided Diagnosis ◽  
Focal Liver Lesions ◽  
Liver Lesions ◽  
Contrast Enhanced ◽  
Computer Aided ◽  
Contrast Enhanced Ultrasonography ◽  
Aided Diagnosis

scholarly journals Computer-Aided Diagnosis in Breast MRI: Do Adjunct Features Derived from T 2-weighted Images Improve Classification of Breast Masses?

2008 ◽  
pp. 11-15
Author(s):  
Willem van Aalst ◽  
Thorsten Twellmann ◽  
Hans Buurman ◽  
Frans A. Gerritsen ◽  
Bart M. ter Haar Romeny
Keyword(s):  
Breast Mri ◽  
Computer Aided Diagnosis ◽  
Breast Masses ◽  
Computer Aided ◽  
Aided Diagnosis

Computer-Aided Diagnosis for the Classification of Breast Masses in Automated Whole Breast Ultrasound Images

2011 ◽  
Vol 37 (4) ◽  
pp. 539-548 ◽  
Author(s):  
Woo Kyung Moon ◽  
Yi-Wei Shen ◽  
Chiun-Sheng Huang ◽  
Li-Ren Chiang ◽  
Ruey-Feng Chang
Keyword(s):  
Computer Aided Diagnosis ◽  
Breast Ultrasound ◽  
Ultrasound Images ◽  
Breast Masses ◽  
Computer Aided ◽  
Aided Diagnosis

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.

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