scholarly journals Breast Cancer Computer-aided Diagnosis System from Digital Mammograms

2019 ◽  
pp. 1-15 ◽  
Author(s):  
Abdulhameed Alkhateeb
Keyword(s):  
Breast Cancer ◽  
Computer Aided Diagnosis ◽  
Forward Selection ◽  
The Public ◽  
Radiological Images ◽  
Computer Aided ◽  
Cade System ◽  
The One ◽  
Sequential Forward Selection ◽  
Aided Diagnosis

Recently, breast cancer is one of the most popular cancers that women could suffer from. The gravity and seriousness of breast cancer can be evidenced by the fact that the mortality rates associated with it are the second highest after lung cancer. For the treatment of breast cancer, Mammography has emerged as the one whose modality when it comes to the defection of this cancer is most effective despite the challenges posed by dense breast parenchyma. In this regard, computer-aided diagnosis (CADe) leverages the mammography systems’ output to facilitate the radiologist’s decision. It can be defined as a system that makes a similar diagnosis to the one done by a radiologist who relies for his/her interpretation on the suggestions generated by a computer after it analyzed a set of patient radiological images when making. Against this backdrop, the current paper examines different ways of utilizing known image processing and techniques of machine learning detection of breast cancer using CAD – more specifically, using mammogram images. This, in turn, helps pathologist in their decision-making process. For effective implementation of this methodology, CADe system was developed and tested on the public and freely available mammographic databases named MIAS database. CADe system is developed to differentiate between normal and abnormal tissues, and it assists radiologists to avoid missing breast abnormalities. The performance of all classifiers is the best by using the sequential forward selection (SFS) method. Also, we can conclude that the quantization grey level of (gray-level co-occurrence matrices) GLCM is a very significant factor to get robust high order features where the results are better with L equal to the size of ROI. Using an enormous number of several features assist the CADe system to be strong enough to distinguish between the different tissues.

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 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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