Deep Learning in Breast Cancer Detection and Classification

Hamed, Ghada; Marey, Mohammed; Amin, Safaa; Tolba, Mohamed F.

doi:10.1007/978-3-030-44289-7_30

Cited by 66 publications

(27 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Hamed et al [ 32 ] proposed using the You Only Look Once (YOLO) and RetinaNet models for breast cancer recognition while achieving 91% accuracy of five mammogram image datasets. Ak [ 33 ] discussed various approaches of machine learning and applied them to the Wisconsin Diagnostic Breast Cancer (WBCD) dataset, focusing on comparative analysis and data visualization.…”

Section: Related Workmentioning

confidence: 99%

Cloud Computing-Based Framework for Breast Cancer Diagnosis Using Extreme Learning Machine

et al. 2021

View full text Add to dashboard Cite

Globally, breast cancer is one of the most significant causes of death among women. Early detection accompanied by prompt treatment can reduce the risk of death due to breast cancer. Currently, machine learning in cloud computing plays a pivotal role in disease diagnosis, but predominantly among the people living in remote areas where medical facilities are scarce. Diagnosis systems based on machine learning act as secondary readers and assist radiologists in the proper diagnosis of diseases, whereas cloud-based systems can support telehealth services and remote diagnostics. Techniques based on artificial neural networks (ANN) have attracted many researchers to explore their capability for disease diagnosis. Extreme learning machine (ELM) is one of the variants of ANN that has a huge potential for solving various classification problems. The framework proposed in this paper amalgamates three research domains: Firstly, ELM is applied for the diagnosis of breast cancer. Secondly, to eliminate insignificant features, the gain ratio feature selection method is employed. Lastly, a cloud computing-based system for remote diagnosis of breast cancer using ELM is proposed. The performance of the cloud-based ELM is compared with some state-of-the-art technologies for disease diagnosis. The results achieved on the Wisconsin Diagnostic Breast Cancer (WBCD) dataset indicate that the cloud-based ELM technique outperforms other results. The best performance results of ELM were found for both the standalone and cloud environments, which were compared. The important findings of the experimental results indicate that the accuracy achieved is 0.9868, the recall is 0.9130, the precision is 0.9054, and the F1-score is 0.8129.

show abstract

Section: Related Workmentioning

confidence: 99%

Cloud Computing-Based Framework for Breast Cancer Diagnosis Using Extreme Learning Machine

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Recurrent Neural Network (RNN) is a class of neural network, that consists of some hidden states, which uses the output of previous state as an input of next state. It can process a sequence of inputs that uses the same parameters at each layer which reduces the complexity of that parameters more accurately than the other neural networks but it can not process a large number of sequence of inputs through ReLU and Tanh activation functions [58], [64].…”

Section: E Recurrent Neural Networkmentioning

confidence: 99%

Prediction of Breast Cancer, Comparative Review of Machine Learning Techniques, and Their Analysis

et al. 2020

View full text Add to dashboard Cite

Breast cancer is type of tumor that occurs in the tissues of the breast. It is most common type of cancer found in women around the world and it is among the leading causes of deaths in women. This paper presents the comparative analysis of machine learning, deep learning and data mining techniques being used for the prediction of breast cancer. Many researchers have put their efforts on breast cancer diagnoses and prognoses, every technique has different accuracy rate and it varies for different situations, tools and datasets being used. Our main focus is to comparatively analyze different existing Machine Learning and Data Mining techniques in order to find out the most appropriate method that will support the large dataset with good accuracy of prediction. The main purpose of this review is to highlight all the previous studies of machine learning algorithms that are being used for breast cancer prediction and this paper provides the all necessary information to the beginners who want to analyze the machine learning algorithms to gain the base of deep learning.

show abstract

“…Also, with the huge number of mammograms screened daily, the process becomes hard, lengthy, complex and time consumable for radiologists and consequently in many cases, it pruned to errors [3]. Since radiologists till now miss between [10%-30%] of cancers either by taking decisions for some cases as benign despite their malignancy (false negative) or requiring additional screens due to their doubt about malignant tumors despite they are benign (false positive) [4], [5].…”

Section: Introductionmentioning

confidence: 99%

Automated Breast Cancer Detection and Classification in Full Field Digital Mammograms Using Two Full and Cropped Detection Paths Approach

et al. 2021

Self Cite

View full text Add to dashboard Cite

Breast cancer is one of the most severe diseases that threaten women's life results in increasing the death rate annually as confirmed by the World Health Organization. Breast cancer early detection is one of the main reasons behind reducing cancer severity. However, with the huge number of mammograms taken daily, the checking process conducted by radiologists becomes lengthy, tiring, and pruning to errors process. Hence, with the tremendous success achieved by utilizing CNNs in bioinformatics, the development of Computer-Aided Detection (CAD) systems has proved its necessity to solve the challenging cases for the biopsies missed by the ordinary checking leads to decreasing the false positive and negative rates. In this paper, we present a YOLOV4 based CAD system to localize lesions in full and cropped mammograms and then classify them to obtain their pathology type. The proposed method mainly consists of three phases that are applied on the full-field digital mammograms of the INbreast dataset. First, the mammograms are preprocessed to remove any extra artifacts and then cropped into small, overlapped slices. Second, masses are localized through two paths: the full mammograms and the cropped slices detection after configuring the YOLO-V4 model. Third, other feature extractors like ResNet, VGG, Inception, etc. are used to classify the localized lesions to compare their performance against YOLO. The proposed method proved using the experimental results the impact of utilizing YOLO-V4 as a detector with the 2-paths of detection of a full mammogram and the cropped slices in a trial to avoid any data loss by resizing the large-sized mammograms. Our system succeeds in detecting the masses' location with an overall accuracy of ≈98% which is more than the recently introduced breast cancer detection methods. Moreover, its ability to distinguish between benign and malignant tumors with an accuracy of ≈95%.

show abstract

Deep Learning in Breast Cancer Detection and Classification

Cited by 66 publications

References 24 publications

Cloud Computing-Based Framework for Breast Cancer Diagnosis Using Extreme Learning Machine

Cloud Computing-Based Framework for Breast Cancer Diagnosis Using Extreme Learning Machine

Prediction of Breast Cancer, Comparative Review of Machine Learning Techniques, and Their Analysis

Automated Breast Cancer Detection and Classification in Full Field Digital Mammograms Using Two Full and Cropped Detection Paths Approach

Contact Info

Product

Resources

About