scholarly journals Hybrid Image Mining Methods to Classify the Abnormality in Complete Field Image Mammograms Based on Normal Regions

2011 ◽  
pp. 80-84
Author(s):  
Aswini Kumar Mohanty ◽  
P. K. Champati ◽  
Manas Rajan Senapati ◽  
Saroj Kumar Lena
Keyword(s):  
Breast Cancer ◽  
Ground Truth ◽  
Developed Countries ◽  
Classification Performance ◽  
Computer Aided Diagnosis ◽  
Common Disease ◽  
Hybrid Technique ◽  
Classification Rate ◽  
Computer Aided ◽  
Aided Diagnosis

Breast Cancer now becomes a common disease among woman in developing as well as developed countries. Many noninvasive methodologies have been used to detect breast cancer. Computer Aided diagnosis through, Mammography is a widely used as a screening tool and is the gold standard for the early detection of breast cancer. The classification of breast masses into the benign and malignant categories is an important problem in the area of computer-aided diagnosis of breast cancer. We present a new method for complete total image of mammogram analysis. A mammogram is analyzed region by region and is classified as normal or abnormal. We present a hybrid technique for extracting features that can be used to distinguish normal and abnormal regions of a mammogram. We describe our classifier technique that uses a unique re-classification method to boost the classification performance. Our proposed hybrid technique comprises decision tree followed by association rule miner shows most proficient and promising performance with high classification rate compared to many other classifiers. We have tested this technique on a set of ground-truth complete total image of mammograms and the result was quite effective.

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.

An automatic Computer-Aided Diagnosis system based on the Multimodal fusion of Breast Cancer (MF-CAD)

2021 ◽  
Vol 69 ◽  
pp. 102914
Author(s):  
Raouia Mokni ◽  
Norhene Gargouri ◽  
Alima Damak ◽  
Dorra Sellami ◽  
Wiem Feki ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Computer Aided Diagnosis ◽  
Multimodal Fusion ◽  
Diagnosis System ◽  
Automatic Computer ◽  
Computer Aided ◽  
Aided Diagnosis

scholarly journals Ratsnake: A Versatile Image Annotation Tool with Application to Computer-Aided Diagnosis

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
D. K. Iakovidis ◽  
T. Goudas ◽  
C. Smailis ◽  
I. Maglogiannis
Keyword(s):  
Kidney Biopsy ◽  
Image Annotation ◽  
Image Force ◽  
Computer Aided Diagnosis ◽  
Image Features ◽  
Obstructive Nephropathy ◽  
Common Disease ◽  
Annotation Tool ◽  
Computer Aided ◽  
Aided Diagnosis

Image segmentation and annotation are key components of image-based medical computer-aided diagnosis (CAD) systems. In this paper we present Ratsnake, a publicly available generic image annotation tool providing annotation efficiency, semantic awareness, versatility, and extensibility, features that can be exploited to transform it into an effective CAD system. In order to demonstrate this unique capability, we present its novel application for the evaluation and quantification of salient objects and structures of interest in kidney biopsy images. Accurate annotation identifying and quantifying such structures in microscopy images can provide an estimation of pathogenesis in obstructive nephropathy, which is a rather common disease with severe implication in children and infants. However a tool for detecting and quantifying the disease is not yet available. A machine learning-based approach, which utilizes prior domain knowledge and textural image features, is considered for the generation of an image force field customizing the presented tool for automatic evaluation of kidney biopsy images. The experimental evaluation of the proposed application of Ratsnake demonstrates its efficiency and effectiveness and promises its wide applicability across a variety of medical imaging domains.

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);

scholarly journals Methods Used in Computer-Aided Diagnosis for Breast Cancer Detection Using Mammograms: A Review

2020 ◽  
Vol 2020 ◽  
pp. 1-21 ◽  
Author(s):  
Saleem Z. Ramadan
Keyword(s):  
Breast Cancer ◽  
Cancer Detection ◽  
The United States ◽  
Computer Aided Diagnosis ◽  
Current Level ◽  
Breast Cancer Detection ◽  
Cad Systems ◽  
Computer Aided ◽  
Detection And Diagnosis ◽  
Aided Diagnosis

According to the American Cancer Society’s forecasts for 2019, there will be about 268,600 new cases in the United States with invasive breast cancer in women, about 62,930 new noninvasive cases, and about 41,760 death cases from breast cancer. As a result, there is a high demand for breast imaging specialists as indicated in a recent report for the Institute of Medicine and National Research Council. One way to meet this demand is through developing Computer-Aided Diagnosis (CAD) systems for breast cancer detection and diagnosis using mammograms. This study aims to review recent advancements and developments in CAD systems for breast cancer detection and diagnosis using mammograms and to give an overview of the methods used in its steps starting from preprocessing and enhancement step and ending in classification step. The current level of performance for the CAD systems is encouraging but not enough to make CAD systems standalone detection and diagnose clinical systems. Unless the performance of CAD systems enhanced dramatically from its current level by enhancing the existing methods, exploiting new promising methods in pattern recognition like data augmentation in deep learning and exploiting the advances in computational power of computers, CAD systems will continue to be a second opinion clinical procedure.

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