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

Ramadan, Saleem Z.

doi:10.1155/2020/9162464

Cited by 80 publications

(61 citation statements)

References 174 publications

(214 reference statements)

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“…The accuracy (97.21%) of the DNNS-based approach, although was better than the other traditional approaches but was not extremely promising. Ramadan et al 33 proposed a computer-aided diagnosis (CAD) system for the detection of breast cancer. The framework involved the use of mammogram data in which were classified for the purpose of disease prediction.…”

Section: Feature Selection Methodsmentioning

confidence: 99%

Multi-modal prediction of breast cancer using particle swarm optimization with non-dominating sorting

Vijayalakshmi¹,

John

Sunder³

et al. 2020

International Journal of Distributed Sensor Networks

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Cancer is enlisted as the second leading reason for death across the world wherein almost one person out of six dies of cancer. Breast cancer is one of the most common forms of cancer predominant in women having the second highest mortality rate in the world. Various scientific studies have been conducted to combat this disease, and machine learning approaches have been an extremely popular choice. Particle swarm optimization has been identified as one of the most powerful and efficient technique for the diagnosis of breast cancer guiding physicians towards timely and accurate treatment. It is also pertinent to mention that multi-modal prediction methods are used to make decisions depending upon different scenarios and aspects whereas the non-dominating sorting feature is useful to sort different objects based on differing requirements. The main novelty of this work is multi-modal prediction algorithm for breast cancer prediction is proposed. The work encompasses the use of particle swarm optimization, non-dominating sorting and multi-classifier techniques, namely, k-nearest neighbour method, fast decision tree and kernel density estimation. Finally, Bayes’ theorem is implemented for revising the results to achieve optimum accuracy in the breast cancer prediction. The proposed particle swarm optimization and non-domination sorting with classifier technique model helps to select the most significant features relevant to breast cancer predictions. The selected features design the objective of the problem model. The proposed model is implemented on the WBCD and WDBC breast cancer data sets publicly available from the UCI machine learning data repository. The metrics considered are sensitivity, specificity, accuracy and time complexity. The experimental results of the study using measures such as sensitivity, specificity, accuracy and time complexity. The experimental results of the study are evaluated against the state-of-the-art algorithms, namely, genetic algorithm kernel density estimation and particle swarm optimization kernel density estimation wherein the results justify the superiority of the proposed model.

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Section: Feature Selection Methodsmentioning

confidence: 99%

Multi-modal prediction of breast cancer using particle swarm optimization with non-dominating sorting

Vijayalakshmi¹,

John

Sunder³

et al. 2020

International Journal of Distributed Sensor Networks

View full text Add to dashboard Cite

show abstract

“…Currently, computer-aided diagnosis (CAD) systems occupy the position of aiding physicians during the process of diagnosis, by easing their workload and reducing the disagreement that stems from the subjective interpretation of pathologists. However, the performance of these systems must be enhanced before they can be considered more dependable than a second-opinion system [ 10 ].…”

Section: Literature Reviewmentioning

confidence: 99%

“…An excellent solution to address the limitations of human diagnosis is computer-aided diagnosis (CAD) systems, which are developed to automatically analyze the WSI and provide a potential diagnosis based on the image. These systems currently contribute to improving efficiency and reducing both the cost of diagnosis and interobserver variability [ 5 , 10 ]. Even though current CAD systems that operate at high sensitivity provide relatively good performance, they will remain a second-opinion clinical procedure until the performance is significantly improved [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

Classification of Hematoxylin and Eosin‐Stained Breast Cancer Histology Microscopy Images Using Transfer Learning with EfficientNets

Munien

Viriri

2021

Computational Intelligence and Neuroscience

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Breast cancer is a fatal disease and is a leading cause of death in women worldwide. The process of diagnosis based on biopsy tissue is nontrivial, time-consuming, and prone to human error, and there may be conflict about the final diagnosis due to interobserver variability. Computer-aided diagnosis systems have been designed and implemented to combat these issues. These systems contribute significantly to increasing the efficiency and accuracy and reducing the cost of diagnosis. Moreover, these systems must perform better so that their determined diagnosis can be more reliable. This research investigates the application of the EfficientNet architecture for the classification of hematoxylin and eosin-stained breast cancer histology images provided by the ICIAR2018 dataset. Specifically, seven EfficientNets were fine-tuned and evaluated on their ability to classify images into four classes: normal, benign, in situ carcinoma, and invasive carcinoma. Moreover, two standard stain normalization techniques, Reinhard and Macenko, were observed to measure the impact of stain normalization on performance. The outcome of this approach reveals that the EfficientNet-B2 model yielded an accuracy and sensitivity of 98.33% using Reinhard stain normalization method on the training images and an accuracy and sensitivity of 96.67% using the Macenko stain normalization method. These satisfactory results indicate that transferring generic features from natural images to medical images through fine-tuning on EfficientNets can achieve satisfactory results.

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“…Also, a recent report from the American Cancer Society's forecast for 2019 predicts that there will be almost 286,600 new cases of invasive breast cancer, about 63,930 new noninvasive cases, and about 41,760 deaths among women in the United States [ 2 ]. This worrisome trend necessitates the need for automated breast cancer detection and diagnosis [ 3 ]. Computer-aided detection or diagnosis (CAD) systems can contribute significantly in the early detection of breast cancer.…”

Section: Introductionmentioning

confidence: 99%

A Semisupervised Learning Scheme with Self-Paced Learning for Classifying Breast Cancer Histopathological Images

Asare

You

Nartey

2020

Computational Intelligence and Neuroscience

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The unavailability of large amounts of well-labeled data poses a significant challenge in many medical imaging tasks. Even in the likelihood of having access to sufficient data, the process of accurately labeling the data is an arduous and time-consuming one, requiring expertise skills. Again, the issue of unbalanced data further compounds the abovementioned problems and presents a considerable challenge for many machine learning algorithms. In lieu of this, the ability to develop algorithms that can exploit large amounts of unlabeled data together with a small amount of labeled data, while demonstrating robustness to data imbalance, can offer promising prospects in building highly efficient classifiers. This work proposes a semisupervised learning method that integrates self-training and self-paced learning to generate and select pseudolabeled samples for classifying breast cancer histopathological images. A novel pseudolabel generation and selection algorithm is introduced in the learning scheme to generate and select highly confident pseudolabeled samples from both well-represented classes to less-represented classes. Such a learning approach improves the performance by jointly learning a model and optimizing the generation of pseudolabels on unlabeled-target data to augment the training data and retraining the model with the generated labels. A class balancing framework that normalizes the class-wise confidence scores is also proposed to prevent the model from ignoring samples from less represented classes (hard-to-learn samples), hence effectively handling the issue of data imbalance. Extensive experimental evaluation of the proposed method on the BreakHis dataset demonstrates the effectiveness of the proposed method.

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

Cited by 80 publications

References 174 publications

Multi-modal prediction of breast cancer using particle swarm optimization with non-dominating sorting

Multi-modal prediction of breast cancer using particle swarm optimization with non-dominating sorting

Classification of Hematoxylin and Eosin‐Stained Breast Cancer Histology Microscopy Images Using Transfer Learning with EfficientNets

A Semisupervised Learning Scheme with Self-Paced Learning for Classifying Breast Cancer Histopathological Images

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