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

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.

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.

scholarly journals An Efficient Breast Cancer Detection Framework for Medical Diagnosis Applications

2022 ◽  
Vol 70 (1) ◽  
pp. 1315-1334
Author(s):  
Naglaa F. Soliman ◽  
Naglaa S. Ali ◽  
Mahmoud I. Aly ◽  
Abeer D. Algarni ◽  
Walid El-Shafai ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Detection ◽  
Medical Diagnosis ◽  
Breast Cancer Detection

scholarly journals Thermogram Breast Cancer Detection: A Comparative Study of Two Machine Learning Techniques

2020 ◽  
Vol 10 (2) ◽  
pp. 551 ◽  
Author(s):  
Fayez AlFayez ◽  
Mohamed W. Abo El-Soud ◽  
Tarek Gaber
Keyword(s):  
Breast Cancer ◽  
Feature Extraction ◽  
Cancer Detection ◽  
Survival Rates ◽  
Histogram Equalization ◽  
Breast Cancer Detection ◽  
Machine Learning Techniques ◽  
World Health ◽  
The World ◽  
Health Organization

Breast cancer is considered one of the major threats for women’s health all over the world. The World Health Organization (WHO) has reported that 1 in every 12 women could be subject to a breast abnormality during her lifetime. To increase survival rates, it is found that it is very effective to early detect breast cancer. Mammography-based breast cancer screening is the leading technology to achieve this aim. However, it still can not deal with patients with dense breast nor with tumor size less than 2 mm. Thermography-based breast cancer approach can address these problems. In this paper, a thermogram-based breast cancer detection approach is proposed. This approach consists of four phases: (1) Image Pre-processing using homomorphic filtering, top-hat transform and adaptive histogram equalization, (2) ROI Segmentation using binary masking and K-mean clustering, (3) feature extraction using signature boundary, and (4) classification in which two classifiers, Extreme Learning Machine (ELM) and Multilayer Perceptron (MLP), were used and compared. The proposed approach is evaluated using the public dataset, DMR-IR. Various experiment scenarios (e.g., integration between geometrical feature extraction, and textural features extraction) were designed and evaluated using different measurements (i.e., accuracy, sensitivity, and specificity). The results showed that ELM-based results were better than MLP-based ones with more than 19%.

scholarly journals Circulating Phospholipids as Biomarkers of Breast Cancer: A Review

2016 ◽  
Vol 10 ◽  
pp. BCBCR.S40693 ◽  
Author(s):  
Dharmica April Haridatt Mistry ◽  
Peter William French
Keyword(s):  
Breast Cancer ◽  
Nucleic Acid ◽  
Early Detection ◽  
Cancer Detection ◽  
Gold Standard ◽  
Cellular Level ◽  
Breast Cancer Tissue ◽  
Breast Cancer Detection ◽  
Cancer Tissue ◽  
Common Cancer

Breast cancer is the most common cancer in women and the second leading cause of cancer deaths in women. The key to surviving breast cancer is early detection and treatment. Current technologies rely heavily on imaging of the breast, and although considered the gold standard, they have their limitations. There is a need for a more accurate screening test for women of all ages, which can detect the cancer at a cellular level and before metastasis. There have been extensive studies into markers for breast cancer including protein and nucleic acid biomarkers, but to date, these have been unsuccessful. A growing field of interest is the association between breast cancer (tissue and cells) and lipids, which is documented in the literature, and may be considered as a leading candidate in the breast cancer detection space.

Digital breast tomosynthesis for breast cancer detection: a diagnostic test accuracy systematic review and meta-analysis

2020 ◽  
Vol 30 (4) ◽  
pp. 2058-2071 ◽  
Author(s):  
Mostafa Alabousi ◽  
Nanxi Zha ◽  
Jean-Paul Salameh ◽  
Lucy Samoilov ◽  
Anahita Dehmoobad Sharifabadi ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Systematic Review ◽  
Diagnostic Test ◽  
Cancer Detection ◽  
Meta Analysis ◽  
Digital Breast Tomosynthesis ◽  
Breast Cancer Detection ◽  
Diagnostic Test Accuracy ◽  
Test Accuracy ◽  
Breast Tomosynthesis

THE OPPORTUNITY OF MAGNETIC INDUCTION TOMOGRAPHY MODALITY IN BREAST CANCER DETECTION

2016 ◽  
Vol 78 (7-4) ◽  
Author(s):  
Gowry Balasena ◽  
Lynn Sim ◽  
Zulkarnay Zakaria ◽  
Shahriman Abu Bakar ◽  
Mohamad Aliff Abd Rahim ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Detection ◽  
Magnetic Induction ◽  
Breast Cancer Detection ◽  
Invasive Technique ◽  
Main Concern ◽  
Medical Field ◽  
Non Invasive ◽  
The World ◽  
Magnetic Induction Tomography

The needs for non-invasive technique in breast cancer detection could enhance and preserve the future of medical field in Malaysia as well as countries around the world. Breast cancer has become the main concern nowadays not only for women but for man as well. In overall, the risk of women getting breast cancer is higher than man due to the denser tissue of breast in women compare to man. Beside the unawareness for the disease, the reason which contributes to this increasing number of breast cancer reported is also due to the limitations arising from modalities such as MRI, Mammography, ultrasound and other modalities. An alternative to current technologies should be improved for early detection and treatment which causes no physical harm to patients if possible. Thus, non-invasive and better technology in detecting breast cancer is very much needed in the current market. This paper will be discussing the insights of Magnetic Induction Tomography techniques in breast cancer detection.

scholarly journals Breast cancer detection in histopathological images using convolutional neural networks

2019 ◽  
Author(s):  
Andrio Rodrigo Corrêa Da Silva ◽  
Iális Cavalcante de Paula Júnior ◽  
Márcio André Baima Amora
Keyword(s):  
Breast Cancer ◽  
Cancer Detection ◽  
Intelligent Systems ◽  
Causes Of Death ◽  
Cross Validation ◽  
Breast Cancer Detection ◽  
Training Data ◽  
The World ◽  
Histopathological Images ◽  
Fold Cross Validation

Breast cancer is one of the biggest causes of death among women around the world. Diagnosing this disease early can offer better treatment to the patient. Intelligent systems have been used for the detection of diseases using images. In this work a convolutional neural network was used for the detection of breast cancer in histopathological images through Keras library and TensorFlow framework. Models were created for 4 datasets with different magnifying factors (40x, 100x, 200x and 400x). Using k-fold cross-validation, it was found that there was a better result for the set of 400x images with 98.44% accuracy in the training data. The set of 200x images obtained a better result for recall and f1-score.

scholarly journals Role of Public Service Awareness Messages in Developing Precautionary Attitude towards Breast Cancer Detection Among Pakistani Women

2020 ◽  
Vol 20 (2) ◽  
pp. 59-78
Author(s):  
Moneeba Iftikhar ◽  
Zahid Yousaf
Keyword(s):  
Breast Cancer ◽  
Cancer Detection ◽  
Public Service ◽  
Breast Cancer Detection ◽  
Fear Appeal ◽  
The Public ◽  
Common Cancer ◽  
Life Threatening ◽  
Precautionary Measures

The study was aimed at scrutinizing the effects of the Public Service Awareness Messages about breast cancer detection among women in terms of developing precautionary measures for the life-threatening disease in order to fight against it before time. Breast Cancer is the most common cancer among the women around the world and especially in Pakistan. The study has been carried out with a survey with 300 women of Lahore, Pakistan and found that Public Service Awareness Messages have been significantly perceived by them. PSAMs are important tool for providing information and spreading awareness regarding the disease. The fear appeal of the messages changed the behavior of the viewers for taking precautionary measures. Public Service Awareness Messages regarding healthcare make people conscious about their health and they believe that precautionary measures can prevent the dreadful comings of this disease.

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