Joint Analysis of Multiple Mammographic Views in CAD Systems for Breast Cancer Detection

2020 ◽

Vol 2020 ◽

pp. 1-21 ◽

Cited By ~ 5

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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Multi-class Breast Cancer Classification using Ensemble of Pretrained models and Transfer Learning

Current Medical Imaging Formerly Current Medical Imaging Reviews ◽

10.2174/1573405617666210218101418 ◽

2021 ◽

Vol 17 ◽

Author(s):

Perumalla Murali Mallikarjuna Rao ◽

Sanjay Kumar Singh ◽

Aditya Khamparia ◽

Bharat Bhushan ◽

Prajoy Podder

Keyword(s):

Breast Cancer ◽

Cancer Detection ◽

Medical Diagnosis ◽

Research Work ◽

Breast Cancer Detection ◽

Test Accuracy ◽

Cloud Platform ◽

Cad Systems ◽

Common Cancer ◽

The World

Aims: Early detection of breast cancer has reduced many deaths. Earlier CAD systems used to be the second opinion for radiologists and clinicians. Machine learning and deep learning has brought tremendous changes in medical diagnosis and imagining. Background: Breast cancer is the most commonly occurring cancer in the women and it is the second most common cancer overall. According to the 2018 statistics, there were over 2million cases all over the world. Belgium and Luxembourg have the highest rate of cancer. Objective: Proposed a method for breast cancer detection using Ensemble learning. 2-class and 8-class classification is performed. Method: To deal with imbalance classification the authors have proposed an ensemble of pretrained models. Result: 98.5% training accuracy and 89% of test accuracy are achieved on 8-class classification. And 99.1% and 98% train and test accuracy are achieved on 2 class classification. Conclusion: It is found that there are high misclassifications in class DC when compared to the other classes, this is due to the imbalance in the dataset. In future, one can increase the size of the datasets or use different methods. In implement this research work, authors have used 2 Nvidia Tesla V100 GPU’s in google cloud platform.

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