NEURAL CLASSIFICATION OF MASS ABNORMALITIES WITH DIFFERENT TYPES OF FEATURES IN DIGITAL MAMMOGRAPHY

2006 ◽  
Vol 06 (01) ◽  
pp. 61-75 ◽  
Author(s):  
R. PANCHAL ◽  
B. VERMA
Keyword(s):  
Breast Cancer ◽  
Research Work ◽  
Breast Cancer Diagnosis ◽  
Classification Rate ◽  
Test Dataset ◽  
Benchmark Database ◽  
Different Types ◽  
Breast Tissues ◽  
Mass Type

Early detection of breast abnormalities remains the primary prevention against breast cancer despite the advances in breast cancer diagnosis and treatment. Presence of mass in breast tissues is highly indicative of breast cancer. The research work presented in this paper investigates the significance of different types of features using proposed neural network based classification technique to classify mass type of breast abnormalities in digital mammograms into malignant and benign. 14 gray level based features, four BI-RADS features, patient age feature and subtlety value feature have been explored using the proposed research methodology to attain maximum classification on test dataset. The proposed research technique attained a 91% testing classification rate with a 100% training classification rate on digital mammograms taken from the DDSM benchmark database.

AUTOMATED CLASSIFICATION OF BREAST CARCINOMA CELL BASED ON IMAGE PROCESSING AND SUPPORT VECTOR MACHINE

2010 ◽  
Vol 22 (02) ◽  
pp. 127-135 ◽  
Author(s):  
Yung-Lung Kuo ◽  
Chien-Chuan Ko ◽  
Yueh-Min Lin ◽  
Yong-Min Chen
Keyword(s):  
Breast Cancer ◽  
Image Processing ◽  
Breast Cancer Diagnosis ◽  
Support Vector ◽  
Automated Classification ◽  
Processing Technologies ◽  
Major Health ◽  
Suitable Treatment ◽  
Breast Tissues

As breast cancer is a substantial threat to the lives of women, it has become a major health issue in the world over the past 50 years, and its incidence has increased in the recent years. Early diagnosis and suitable treatment is relatively important. In the process of breast screening, tissue biopsy is an important operation in determining the presence of breast cancer. It not only provides an accurate diagnosis of the disease but also determines the prognosis for breast cancer. The main goal of this study is to develop a breast cancer diagnosis system based on histopathology and a sequence of image-processing technologies to analyze H&E stained images of breast tissues. The proposed system can automatically detect the mitosis of nuclei and analyze the size and the shape of nuclei to evaluate the duct structure of the breast tissue. Moreover, it provides physicians quantitative prognosis and classification of tissue malignancy, which will improve the diagnostic accuracy and efficiency of the cancer.

NEURAL-ASSOCIATION OF MICROCALCIFICATION PATTERNS FOR THEIR RELIABLE CLASSIFICATION IN DIGITAL MAMMOGRAPHY

2006 ◽  
Vol 20 (07) ◽  
pp. 971-983 ◽  
Author(s):  
RINKU PANCHAL ◽  
BRIJESH VERMA
Keyword(s):  
Breast Cancer ◽  
Early Detection ◽  
Digital Mammography ◽  
Research Work ◽  
Classification Rate ◽  
Neural Network Approach ◽  
Testing Dataset ◽  
New Information ◽  
Reliable Classification

Breast cancer continues to be the most common cause of cancer deaths in women. Early detection of breast cancer is significant for better prognosis. Digital Mammography currently offers the best control strategy for the early detection of breast cancer. The research work in this paper investigates the significance of neural-association of microcalcification patterns for their reliable classification in digital mammograms. The proposed technique explores the auto-associative abilities of a neural network approach to regenerate the composite of its learned patterns most consistent with the new information, thus the regenerated patterns can uniquely signify each input class and improve the overall classification. Two types of features: computer extracted (gray level based statistical) features and human extracted (radiologists' interpretation) features are used for the classification of calcification type of breast abnormalities. The proposed technique attained the highest 90.5% classification rate on the calcification testing dataset.

Classification of Masses in Digital Mammograms Using the Genetic Ensemble Method

2018 ◽  
Vol 29 (1) ◽  
pp. 831-845 ◽  
Author(s):  
Shankar Thawkar ◽  
Ranjana Ingolikar
Keyword(s):  
Breast Cancer ◽  
Feature Selection ◽  
Cross Validation ◽  
Research Work ◽  
Feature Space ◽  
Breast Cancer Diagnosis ◽  
Ensemble Method ◽  
Adaptive Boosting ◽  
Fold Cross Validation

Abstract All over the world, breast cancer is the second leading cause of death in women above 40 years of age. To design an efficient classification system for breast cancer diagnosis, one has to use efficient algorithms for feature selection to reduce the feature space of mammogram classification. The current work investigates the use of hybrid genetic ensemble method for feature selection and classification of masses. Genetic algorithm (GA) is used to select a subset of features and to evaluate the fitness of the selected features, Adaptive boosting (AdaBoost) and Random Forest (RF) ensembles with 10-fold cross-validation are employed. The selected features are used to classify masses into benign or malignant using AdaBoost, RF, and single Decision Tree (DT) classifiers. The performance evaluation of classifiers indicates that AdaBoost outperforms both RF and single DT classifiers. AdaBoost achieves an accuracy of 96.15%, with 97.32% sensitivity, 95.90% specificity, and area under curve of AZ = 0.982 ± 0.004. The results obtained with the proposed method are better when compared with extant research work.

scholarly journals 3D-semantic segmentation and classification of stomach infections using uncertainty aware deep neural networks

2021 ◽  
Author(s):  
Javaria Amin ◽  
Muhammad Sharif ◽  
Eman Gul ◽  
Ramesh Sunder Nayak
Keyword(s):  
Research Work ◽  
Semantic Segmentation ◽  
Gastrointestinal Infections ◽  
Method Performance ◽  
Low Contrast ◽  
Proposed Model ◽  
Different Types ◽  
Automated Technique ◽  
Wireless Capsule

AbstractWireless capsule endoscopy (WCE) might move through human body and captures the small bowel and captures the video and require the analysis of all frames of video due to which the diagnosis of gastrointestinal infections by the physician is a tedious task. This tiresome assignment has fuelled the researcher’s efforts to present an automated technique for gastrointestinal infections detection. The segmentation of stomach infections is a challenging task because the lesion region having low contrast and irregular shape and size. To handle this challenging task, in this research work a new deep semantic segmentation model is suggested for 3D-segmentation of the different types of stomach infections. In the segmentation model, deep labv3 is employed as a backbone of the ResNet-50 model. The model is trained with ground-masks and accurately performs pixel-wise classification in the testing phase. Similarity among the different types of stomach lesions accurate classification is a difficult task, which is addressed in this reported research by extracting deep features from global input images using a pre-trained ResNet-50 model. Furthermore, the latest advances in the estimation of uncertainty and model interpretability in the classification of different types of stomach infections is presented. The classification results estimate uncertainty related to the vital features in input and show how uncertainty and interpretability might be modeled in ResNet-50 for the classification of the different types of stomach infections. The proposed model achieved up to 90% prediction scores to authenticate the method performance.

Elasto-Mammography: Elastic Property Reconstruction in Breast Tissues

2005 ◽  
Vol 874 ◽  
Author(s):  
Z. Wang ◽  
Y. Liu ◽  
L.Z. Sun ◽  
G. Wang
Keyword(s):  
Breast Cancer ◽  
Cancer Diagnosis ◽  
Imaging Modality ◽  
Breast Cancer Diagnosis ◽  
Reconstruction Method ◽  
Breast Cancers ◽  
Primary Method ◽  
Conventional Mammography ◽  
Breast Tissues ◽  
Higher Sensitivity

AbstractMammography is the primary method for screening and detecting breast cancers. However, it frequently fails to detect small tumors and is not quite specific in terms of tumor benignity and malignancy. The objective of this paper is to develop a new imaging modality called elastomammography that generates the modulus elastograms based conventional mammographs. A new elastic reconstruction method is described based on elastography and mammography for breast tissues. Elastic distribution can be reconstructed through the measurement of displacement provided by mammographic projection. It is shown that the proposed elasto-mammography provides higher sensitivity and specificity than the conventional mammography on its own for breast cancer diagnosis.

scholarly journals A Case of Breastfeeding With Active Breast Cancer

2019 ◽  
Vol 10 (3) ◽  
pp. 136-140 ◽  
Author(s):  
Erika Gergerich ◽  
Bethany Garling-Spychala
Keyword(s):  
Breast Cancer ◽  
Breast Milk ◽  
Cancer Treatment ◽  
Cancer Diagnosis ◽  
Breast Cancer Diagnosis ◽  
Donor Milk ◽  
Geographic Barriers ◽  
Different Types ◽  
Active Cancer

IntroductionPregnancy-associated breast cancer is relatively uncommon, with few guidelines for management. Women with an active breast cancer diagnosis who wish to offer their children breast milk (either first or second hand) face a number of obstacles and gaps in information.MethodThis article presents a case study and summary of current research on the topic of breastfeeding and breast cancer.Results and DiscussionDifferent types of cancer and cancer treatment influence whether a woman will be able to breastfeed. Some mothers can resume breastfeeding after treatment. If treatment is lengthy, breastfeeding may need to cease permanently. Mothers may need to take medications to help them wean. Finally some mothers may use donor milk to feed their babies once they are no longer able to breastfeed.ConclusionsFurther research is needed to determine and formalize guidelines related to the safety of breastfeeding with an active cancer diagnosis. And there is a need for increased access to breast milk for mothers who are unable to breastfeed. There are geographic barriers, as well as obstacles related to availability and cost.

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