Automated breast cancer lesion detection on breast MRI using artificial intelligence.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e14612-e14612 ◽  
Author(s):  
Nataly Tapia Negrete ◽  
Ruquaiyah Takhtawala ◽  
Madeleine Shaver ◽  
Turkay Kart ◽  
Yang Zhang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Artificial Intelligence ◽  
Breast Tumor ◽  
Breast Mri ◽  
Lesion Detection ◽  
Tumor Segmentation ◽  
Dice Similarity Coefficient ◽  
Data Set ◽  
Post Contrast

e14612 Background: Over 40,000 women in the US will die from breast cancer. Early detection of cancer is crucial and is a potential avenue to improve survival. The objective of this research study is to develop a convoluted neural network (CNN), a subset of artificial intelligence, in order to enhance computerized detection of breast lesions on MRIs. Methods: This is an institutional review board approved retrospective study with post contrast MRI data from 238 patients. Breast tumor segmentation was automated with a hybrid 3D/2D CNN designed adapted from U-net, a popular neural network architecture in biomedical image analysis. T1 post-contrast MRI volumes were used to train the network. The data set was separated into training (80%) and validation (20%) sets. Re-sampling and normalization using z-scores were applied to each volume before training. Contracting and expanding arms of the model consist of successive convolutions followed by batch normalization and ReLU operations. Ground truth was established through manual segmentation and previously conducted readings of the images used to train our network. Results: A 5-fold cross validation was performed for analysis. The Dice similarity coefficient was used to assess segmentation accuracy. The hybrid 3D/2D U-Net architecture yielded a Dice score of 0.753 and a Pearson correlation of 0.548 for the breast tumor segmentation. Conclusions: These results demonstrated the feasibility for artificial intelligence applications in accurately identifying the presence of lesions on breast MRI images.

scholarly journals Prediction of Breast Tumor Malignancy Using Neural Network and Whale Optimization Algorithms (WOA)

2019 ◽  
Vol 12 (3) ◽  
pp. 26-35
Author(s):  
Ali Sharifi
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Machine Learning ◽  
Early Diagnosis ◽  
Health Care Providers ◽  
Breast Tumor ◽  
Care Providers ◽  
Data Set ◽  
Whale Optimization ◽  
Effective Decision Making

Introduction: Breast cancer is the most prevalent cause of cancer mortality among women. Early diagnosis of breast cancer gives patients greater survival time. The present study aims to provide an algorithm for more accurate prediction and more effective decision-making in the treatment of patients with breast cancer. Methods: The present study was applied, descriptive-analytical, based on the use of computerized methods. We obtained 699 independent records containing nine clinical variables from the UCI machine learning. The EM algorithm was used to analyze the data before normalizing them. Following that, a combination of neural network model based on multilayer perceptron structure with the Whale Optimization Algorithm (WOA) was used to predict the breast tumor malignancy. Results: After preprocessing the disease data set and reducing data dimensions, the accuracy of the proposed algorithm for training and testing data was 99.6% and 99%, respectively. The prediction accuracy of the proposed model was 99.4%, which would be a satisfying result compared to different methods of machine learning in other studies. Conclusion: Considering the importance of early diagnosis of breast cancer, the results of this study may have highly useful implications for health care providers and planners so as to achieve the early diagnosis of the disease.

scholarly journals Evaluation of a Nonrigid Motion Compensation Technique Based on Spatiotemporal Features for Small Lesion Detection in Breast MRI

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
F. Steinbruecker ◽  
A. Meyer-Baese ◽  
T. Schlossbauer ◽  
D. Cremers
Keyword(s):  
Breast Cancer ◽  
Motion Compensation ◽  
Rigid Motion ◽  
Breast Mri ◽  
Lesion Detection ◽  
Nonrigid Motion ◽  
Small Lesion ◽  
Spatiotemporal Features ◽  
Optical Flow Method ◽  
Compensation Technique

Motion-induced artifacts represent a major problem in detection and diagnosis of breast cancer in dynamic contrast-enhanced magnetic resonance imaging. The goal of this paper is to evaluate the performance of a new nonrigid motion correction algorithm based on the optical flow method. For each of the small lesions, we extracted morphological and dynamical features describing both global and local shape, and kinetics behavior. In this paper, we compare the performance of each extracted feature set under consideration of several 2D or 3D motion compensation parameters for the differential diagnosis of enhancing lesions in breast MRI. Based on several simulation results, we determined the optimal motion compensation parameters. Our results have shown that motion compensation can improve the classification results. The results suggest that the computerized analysis system based on the non-rigid motion compensation technique and spatiotemporal features has the potential to increase the diagnostic accuracy of MRI mammography for small lesions and can be used as a basis for computer-aided diagnosis of breast cancer with MR mammography.

Logistic Regression and Artificial Neural Network: A Comparative Study in Diagnosing Breast Cancer

2021 ◽  
pp. 6330-6334
Author(s):  
W. Abdul Hameed ◽  
Anuradha D. ◽  
Kaspar S.
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Artificial Neural Network ◽  
Logistic Regression ◽  
Breast Tumor ◽  
Diagnostic Performance ◽  
Malignant Breast Tumor ◽  
Malignant Breast ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Breast tumor is a common problem in gynecology. A reliable test for preoperative discrimination between benign and malignant breast tumor is highly helpful for clinicians in culling the malignant cells through felicitous treatment for patients. This paper is carried out to generate and estimate both logistic regression technique and Artificial Neural Network (ANN) technique to predict the malignancy of breast tumor, utilizing Wisconsin Diagnosis Breast Cancer Database (WDBC). Our aim in this Paper is: (i) to compare the diagnostic performance of both methods in distinguishing between malignant and benign patterns, (ii) to truncate the number of benign cases sent for biopsy utilizing the best model as an auxiliary implement, and (iii) to authenticate the capability of each model to recognize incipient cases as an expert system.

Layer-Wise Tumor Segmentation of Breast Images Using Convolutional Neural Networks

2021 ◽  
pp. 70-84
Author(s):  
Nishanth Krishnaraj ◽  
A. Mary Mekala ◽  
Bhaskar M. ◽  
Ruban Nersisson ◽  
Alex Noel Joseph Raj
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Network Architecture ◽  
Breast Imaging ◽  
Breast Tissue ◽  
Imaging Techniques ◽  
Tumor Segmentation ◽  
Cancer Type ◽  
The Neural Network ◽  
Life Threatening

Early prediction of cancer type has become very crucial. Breast cancer is common to women and it leads to life threatening. Several imaging techniques have been suggested for timely detection and treatment of breast cancer. More research findings have been done to accurately detect the breast cancer. Automated whole breast ultrasound (AWBUS) is a new breast imaging technology that can render the entire breast anatomy in 3-D volume. The tissue layers in the breast are segmented and the type of lesion in the breast tissue can be identified which is essential for cancer detection. In this chapter, a u-net convolutional neural network architecture is used to implement the segmentation of breast tissues from AWBUS images into the different layers, that is, epidermis, subcutaneous, and muscular layer. The architecture was trained and tested with the AWBUS dataset images. The performance of the proposed scheme was based on accuracy, loss and the F1 score of the neural network that was calculated for each layer of the breast tissue.

scholarly journals Semi-automatic segmentation from intrinsically-registered 18F-FDG–PET/MRI for treatment response assessment in a breast cancer cohort: comparison to manual DCE–MRI

2019 ◽  
Vol 33 (2) ◽  
pp. 317-328
Author(s):  
Maren Marie Sjaastad Andreassen ◽  
Pål Erik Goa ◽  
Torill Eidhammer Sjøbakk ◽  
Roja Hedayati ◽  
Hans Petter Eikesdal ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Treatment Response ◽  
Neoadjuvant Treatment ◽  
Gaussian Mixture ◽  
Tumor Segmentation ◽  
Dice Similarity Coefficient ◽  
Adc Value ◽  
Dce Mri ◽  
18F Fdg ◽  
Tumor Area

Abstract Objectives To investigate the reliability of simultaneous positron emission tomography and magnetic resonance imaging (PET/MRI)-derived biomarkers using semi-automated Gaussian mixture model (GMM) segmentation on PET images, against conventional manual tumor segmentation on dynamic contrast-enhanced (DCE) images. Materials and methods Twenty-four breast cancer patients underwent PET/MRI (following 18F-fluorodeoxyglucose (18F-FDG) injection) at baseline and during neoadjuvant treatment, yielding 53 data sets (24 untreated, 29 treated). Two-dimensional tumor segmentation was performed manually on DCE–MRI images (manual DCE) and using GMM with corresponding PET images (GMM–PET). Tumor area and mean apparent diffusion coefficient (ADC) derived from both segmentation methods were compared, and spatial overlap between the segmentations was assessed with Dice similarity coefficient and center-of-gravity displacement. Results No significant differences were observed between mean ADC and tumor area derived from manual DCE segmentation and GMM–PET. There were strong positive correlations for tumor area and ADC derived from manual DCE and GMM–PET for untreated and treated lesions. The mean Dice score for GMM–PET was 0.770 and 0.649 for untreated and treated lesions, respectively. Discussion Using PET/MRI, tumor area and mean ADC value estimated with a GMM–PET can replicate manual DCE tumor definition from MRI for monitoring neoadjuvant treatment response in breast cancer.

scholarly journals Using new artificial bee colony as probabilistic neural network for breast cancer data classification

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Habib Shah
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Artificial Bee Colony ◽  
Probabilistic Neural Network ◽  
Breast Cancers ◽  
Breast Cancer Data ◽  
Data Set ◽  
Content Type ◽  
Cancer Data ◽  
Bee Colony

PurposeBreast cancer is an important medical disorder, which is not a single disease but a cluster more than 200 different serious medical complications.Design/methodology/approachThe new artificial bee colony (ABC) implementation has been applied to probabilistic neural network (PNN) for training and testing purpose to classify the breast cancer data set.FindingsThe new ABC algorithm along with PNN has been successfully applied to breast cancers data set for prediction purpose with minimum iteration consuming.Originality/valueThe new implementation of ABC along PNN can be easily applied to times series problems for accurate prediction or classification.

Grey Wolf Optimization Trained Feed Foreword Neural Network for Breast Cancer Classification

2018 ◽  
Vol 5 (2) ◽  
pp. 21-29 ◽  
Author(s):  
Shankho Subhra Pal
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Cancer Classification ◽  
Grey Wolf ◽  
Hospital Data ◽  
Grey Wolf Optimization ◽  
Data Set ◽  
Breast Cancer Classification ◽  
Cause Of Deaths ◽  
Magnetic Resonance Imaging Mri

Breast cancer is the most common invasive cancer in females worldwide and is major cause of deaths. The diagnoses of breast cancer include mammograms, breast ultrasound, magnetic resonance imaging (MRI), ductogram and biopsy. Biopsy is best and only way to know if the breast tumor is cancerous. Report says that positive detection of breast cancer through biopsy can reach as low as 10%. So many statistical techniques and cognitive science approaches like artificial intelligence are used to detect the type of breast cancer in a patient for getting more accuracy. This article presents the breast cancer classification using feed foreword neural network trained by grey wolf optimization algorithm. The superiority of the GWO-FFNN is shown by experimenting Wisconsin Hospital data set (Breast Cancer Wisconsin) and comparing recently reported results. The evaluations show that the proposed approach is very robust, effective and gives better correct classification as compared to other classifiers.

scholarly journals Convolution Based Neural Network Model to Prevent Diseases Classification and Prediction Using Deep Learning

2021 ◽  
Vol 10 (8) ◽  
pp. 46-50
Author(s):  
D J Samatha Naidu ◽  
M.Gurivi Reddy
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Deep Learning ◽  
The Internet ◽  
Learning Models ◽  
Data Set ◽  
Proposed Model ◽  
Recent Developments ◽  
Automatic Image Recognition ◽  
Recognition Systems

The farmer is a backbone to nation, but majority of the cultivated crops in india affecting by various diseases at various stages of its cultivation. Recent research works shows that diseases are not providing accurate results and few identifying but not providing optimized solutions to the system. In proposed work, the recent developments of Artificial intelligence through Deep Learning show that AIR (Automatic Image Recognition systems) using CNN algorithm models can be very beneficial in such scenarios. The Rice leaf diseases images related dataset is not easily available to automate , so that we have created our own trained data set which is small in size hence we have used transfer learning to develop our Proposed model which supports deep learning models. The Proposed CNN architecture illustrated based on VGG-16 model and it is trained, tested on given dataset collected from rice fields and the internet. The accuracy of the proposed model is moderately accurate with 92.46%.

scholarly journals Prediction and Prognosis of Acute Myocardial Infarction in Patients with Previous Coronary Artery Bypass Grafting Using Neural Network Model (Preprint)

2019 ◽  
Author(s):  
Predrag Mitrovic ◽  
Branislav Stefanovic ◽  
Mina Radovanovic ◽  
Nebojsa Radovanovic ◽  
Dubravka Rajic ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Artificial Neural Network ◽  
Coronary Artery ◽  
Coronary Artery Bypass ◽  
Artery Bypass ◽  
Bypass Grafting ◽  
Data Set

BACKGROUND Patients with previous coronary artery bypass grafting represent a substantial percentage of the total population of patients with acute myocardial infarction. Prognosis of the future disease expression is an important part in the follow-up of patients with previous CABG. It is well known that outcome of patients with previous CABG influenced with a lot of abnormalities. Neural networks are a form of artificial intelligence and they may obviate some of the problems associated with traditional statistical techniques, and they are representing a major advance in predictive modeling. OBJECTIVE The purpose of this study was to assess the usefulness and accuracy of artificial neural network in the prediction and prognosis of acute myocardial infarction in patients with previous coronary artery bypass surgery. METHODS The baseline characteristics and clinical data were recorded in 2180 consecutive patients. The data set contains 13 predictor variables per patient. It was first randomly split into training (1090 cases) and test sets (1090 cases). Artificial neural network performance was evaluated using the original data set for each network, as well as its complementary test data set, containing patient data not used for training the network. The program compared actual with predict outcome for each patient, generating a file of comparative results. At the end, results from this file were analyzed and compared, on the basis of a 2x2 contingency table constructed from expected or obtained statistics (accuracy, sensitivity, specificity and positive/negative predictivity). RESULTS Linear discriminant analysis was not efficient for prediction and prognosis of acute myocardial infarction in patients with prior CABG. The results show that a statistical linear model is not able to perform class separation in multidimensional space and that a nonlinear approach is justified. In analyzing the performance of neural network in outcome prognosis of AMI in patients with previous CABG it is clear that neural network method was better for almost all statistic parameters for all analyzed prediction variables. CONCLUSIONS In this clinical situation, artificial intelligence appears to be superior to linear methods for prediction and prognosis of AMI in patients with previous CABG.

scholarly journals Deep Weber Dominant Local Order Based Feature Generator and Improved Convolution Neural Network for Brain Tumor Segmentation in MR Images

2020 ◽  
Vol 9 (3) ◽  
pp. 3150-3157
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Brain Tumor ◽  
Median Filter ◽  
Histogram Equalization ◽  
Convolution Neural Network ◽  
Tumor Segmentation ◽  
Local Order ◽  
Dice Similarity Coefficient ◽  
Brain Tumor Segmentation

This paper introduces a scheme for retrieving deep features to carry out the procedure of recognising brain tumors from MR image. Initially, the MR brain image is denoised through the Modified Decision Based Unsymmetric Trimmed Median Filter (MDBUTMF) after that the contrast of the image is improved through Contrast Limited Adaptive Histogram Equalization (CLAHE). Once the pre-processing task is completed, the next phase is to extract the feature. In order to acquire the features of pre-processed images, this article offers a feature extraction technique named Deep Weber Dominant Local Order Based Feature Generator (DWDLOBFG). Once the deep features are retrieved, the next stage is to separate the brain tumor. Improved Convolution Neural Network (ICNN) is used to achieve this procedure. To explore the efficiency of deep feature extraction and in-depth machine learning methods, four performance indicators were used: Sensitivity (SEN), Jaccard Index (JI), Dice Similarity Coefficient (DSC) and Positive Predictive Value (PPV). The investigational outputs illustrated that the DWDLOBFG and ICNN achieve best outputs than existing techniques.

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