scholarly journals Filtering Method for Pre-processing Mammogram Images for Breast Cancer Detection

2019 ◽  
Vol 9 (1) ◽  
pp. 4222-4229
Keyword(s):  
Breast Cancer ◽  
Cancer Detection ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Breast Cancer Detection ◽  
Disease Classification ◽  
High Rate ◽  
Medical Systems ◽  
Mammogram Images ◽  
Filtering Techniques

Breast cancer is a stand-out surrounded by the most widely perceived diseases and has a high rate of mortality around the world, significantly risking the health of the females. Among existing all modalities of medical scans, mammography is the most preferred modality for preliminary examination of breast cancer. To assist radiologist, a computer-aided diagnosis (CAD) is enhancing and important medical systems for mammographic lesion analysis. CAD is necessary to provide doctors, to improve detection quality of breast cancer. In mammogram images, micro-calcifications is one of the imperative sign for breast cancer detection. Mammographic medical scan may present unwanted noise and CAD systems are very sensitive to noise. So, pre-processing of medical images for any medical image analysis application like brain tumor detection, breast cancer detection, and interstitial lung disease classification is considered as an important step. The segmentation or classification accuracy is mainly depends upon the significant improved pre-processing process. Thus, in this work, different types of filtering techniques used for noise reduction in medical image processing are analyzed. The qualitative and quantitative results are examined on mini-MIAS mammogram image database. The effectiveness of filtering techniques is compared based on the different quantitative parameters and visual qualities of examined output.

scholarly journals OPTIMIZED DEEP NEURAL NETWORKS ARCHITECTURE MODEL FOR BREAST CANCER DIAGNOSIS

YMER Digital ◽  
2021 ◽  
Vol 20 (11) ◽  
pp. 161-175
Author(s):  
G Kanimozhi ◽  
◽  
P Shanmugavadivu ◽  
Keyword(s):  
Breast Cancer ◽  
Neural Networks ◽  
Cancer Detection ◽  
Deep Neural Networks ◽  
Medical Image Analysis ◽  
Optimization Techniques ◽  
Breast Cancer Detection ◽  
Automated System ◽  
Stochastic Gradient Descent ◽  
Architecture Model

Breast cancer has increasingly claimed the lives of women. Oncologists use digital mammograms as a viable source to detect breast cancer and classify it into benign and malignant based on the severity. The performance of the traditional methods on breast cancer detection could not be improved beyond a certain point due to the limitations and scope of computing. Moreover, the constrained scope of image processing techniques in developing automated breast cancer detection systems has motivated the researchers to shift their focus towards Artificial Intelligence based models. The Neural Networks (NN) have exhibited greater scope for the development of automated medical image analysis systems with the highest degree of accuracy. As NN model enables the automated system to understand the feature of problem-solving without being explicitly programmed. The optimization for NN offers an additional payoff on accuracy, computational complexity, and time. As the scope and suitability of optimization methods are data-dependent, the choice of selection of an appropriate optimization method itself is emerging as a prominent domain of research. In this paper, Deep Neural Networks (DNN) with different optimizers and Learning rates were designed for the prediction of breast cancer and its classification. Comparative performance analysis of five distinct first-order gradient-based optimization techniques, namely, Adaptive Gradient (Adagrad), Root Mean Square Propagation (RMSProp), Adaptive Delta (Adadelta), Adaptive Moment Estimation (Adam), and Stochastic Gradient Descent (SGD), is carried out to make predictions on the classification of breast cancer masses. For this purpose, the Mammographic Mass dataset was chosen for experimentation. The parameters determined for experiments were chosen on the number of hidden layers and learning rate along with hyperparameter tuning. The impacts of those optimizers were tested on the NN with One Hidden Layer (NN1HL), DNN with Three Hidden Layers (DNN4HL), and DNN with Eight Hidden Layers (DNN8HL). The experimental results showed that DNN8HL-Adam (DNN8HL-AM) had produced the highest accuracy of 91% among its counterparts. This research endorsed that the incorporation of optimizers in DNN contributes to an increased accuracy and optimized architecture for automated system development using neural networks.

scholarly journals Breast Cancer Detection, Diagnosis, and Prediction

2020 ◽  
Vol 9 (6) ◽  
pp. 38-42
Keyword(s):  
Breast Cancer ◽  
Cancer Detection ◽  
Breast Imaging ◽  
Malignant Tumors ◽  
Region Of Interest ◽  
Breast Cancer Detection ◽  
Support Vector ◽  
Svm Classifier ◽  
Cad System ◽  
Mammogram Images

The early detection, diagnosis, prediction, and treatment of breast cancer are challenginghealthcare problems. This study focuses on outlining the traditional and trending techniques used for breast cancer detection, diagnosis, and prediction, including trending noninvasive, nonionizing, and biomarker genetic techniques.In addition, a Computer Aided Detection (CAD) is introduced to classify benign and malignant tumors in mammograms. This CAD system involves three steps. First, the Region of Interest (ROI) that includesthe tumor is identified using a threshold-based method. Second, a deep learning Convolutional Neural Network (CNN) processes the ROI to extract relevant mammogram features. Finally, a Support Vector Machine (SVM) classifier is used to decode two classes of mammogram structures (i.e., Benign (B), and Malignant (M) nodules). The training processes and implementations were carried out using 2800 mammogram images taken from the Curated Breast Imaging Subset of DDSM (CBIS-DDSM). Results have shown that the accuracy of CNN-SVM system achieves 85.1% using AlexNet CNN. Comparison with related work shows the promise of the proposed CAD system

scholarly journals Acceleration of Image Segmentation Algorithm for (Breast) Mammogram Images Using High-Performance Reconfigurable Dataflow Computers

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Ivan L. Milankovic ◽  
Nikola V. Mijailovic ◽  
Nenad D. Filipovic ◽  
Aleksandar S. Peulic
Keyword(s):  
Breast Cancer ◽  
Image Segmentation ◽  
Cancer Detection ◽  
High Performance ◽  
Region Of Interest ◽  
Breast Cancer Detection ◽  
Large Sets ◽  
Breast Image ◽  
Mammogram Images ◽  
Image Segmentation Algorithm

Image segmentation is one of the most common procedures in medical imaging applications. It is also a very important task in breast cancer detection. Breast cancer detection procedure based on mammography can be divided into several stages. The first stage is the extraction of the region of interest from a breast image, followed by the identification of suspicious mass regions, their classification, and comparison with the existing image database. It is often the case that already existing image databases have large sets of data whose processing requires a lot of time, and thus the acceleration of each of the processing stages in breast cancer detection is a very important issue. In this paper, the implementation of the already existing algorithm for region-of-interest based image segmentation for mammogram images on High-Performance Reconfigurable Dataflow Computers (HPRDCs) is proposed. As a dataflow engine (DFE) of such HPRDC, Maxeler’s acceleration card is used. The experiments for examining the acceleration of that algorithm on the Reconfigurable Dataflow Computers (RDCs) are performed with two types of mammogram images with different resolutions. There were, also, several DFE configurations and each of them gave a different acceleration value of algorithm execution. Those acceleration values are presented and experimental results showed good acceleration.

scholarly journals Systematic Review of Computing Approaches for Breast Cancer Detection Based Computer Aided Diagnosis Using Mammogram Images

2021 ◽  
pp. 1-47
Author(s):  
Dilovan Asaad Zebari ◽  
Dheyaa Ahmed Ibrahim ◽  
Diyar Qader Zeebaree ◽  
Habibollah Haron ◽  
Merdin Shamal Salih ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Systematic Review ◽  
Cancer Detection ◽  
Computer Aided Diagnosis ◽  
Breast Cancer Detection ◽  
Computer Aided ◽  
Aided Diagnosis ◽  
Mammogram Images

scholarly journals A Research on Breast Cancer Detection using Mammography

2019 ◽  
Vol 9 (1) ◽  
pp. 3459-3463
Keyword(s):  
Breast Cancer ◽  
Cancer Detection ◽  
Breast Cancer Detection ◽  
Basic Knowledge ◽  
Efficient Manner ◽  
The Past ◽  
Different Types ◽  
Common Understanding ◽  
Mammogram Images

The digital mammogram has developed as the standard screening approach for breast cancer detection and further defects in human breast tissue problem. Early detection is an efficient manner to decrease mortality in worldwide. In the past decades, several researchers implemented many methods to consistently identify the breast cancer by mammogram images. Those methods were employed to produce systems to support radiologists and physicians attain more accurate diagnosis. Accurate segmentation and classification of various tumors in the mammography plays a complex role in the early diagnosis of breast cancer. This paper defines the research on Breast Cancer Detection (BCD) methods which includes two major steps such as segmentation and classification. This research presented the different types of BCD methods with their main contributions. Additionally, it assists the researchers in the area of breast cancer detection by providing the basic knowledge and common understanding of the newest BCD methods.

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