An Automatic Computer-Aided Diagnosis System for Breast Cancer in Digital Mammograms via Deep Belief Network

Al-antari, Mugahed A.; Al‐masni, Mohammed A.; Park, Sung Un; Park, Jun Hyeok; Metwally, Mohamed K.; Kadah, Yasser M.; Han, Sangheon

doi:10.1007/s40846-017-0321-6

Cited by 107 publications

(59 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [14], Al-antari et al proposed a deep belief network (DBN)-based CAD system to classify breast tissues. Three classes are used in the classification process, which are normal, benign, and malignant.…”

Section: B Taxonomy Of Ai-based Approaches For Breast Cancer Diagnosismentioning

confidence: 99%

“…From a medical point of view, the early detection of breast cancer contributes to saving the lives of patients as well as decreasing the cost of treatment at both the private and governmental medical institution levels. Computer science researchers have employed AI for this purpose, and many approaches have been proposed, such as [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21]. However, the quality of any proposed approach for breast cancer detection is evaluated based on its accuracy.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Enhanced Artificial Intelligence System for Diagnosing and Predicting Breast Cancer using Deep Learning

Alfifi¹,

Alrahhal²,

Bataineh³

et al. 2020

IJACSA

View full text Add to dashboard Cite

Breast cancer is the leading cause of death among women with cancer. Computer-aided diagnosis is an efficient method for assisting medical experts in early diagnosis, improving the chance of recovery. Employing artificial intelligence (AI) in the medical area is very crucial due to the sensitivity of this field. This means that the low accuracy of the classification methods used for cancer detection is a critical issue. This problem is accentuated when it comes to blurry mammogram images. In this paper, convolutional neural networks (CNNs) are employed to present the traditional convolutional neural network (TCNN) and supported convolutional neural network (SCNN) approaches. The TCNN and SCNN approaches contribute by overcoming the shift and scaling problems included in blurry mammogram images. In addition, the flipped rotation-based approach (FRbA) is proposed to enhance the accuracy of the prediction process (classification of the type of cancerous mass) by taking into account the different directions of the cancerous mass to extract effective features to form the map of the tumour. The proposed approaches are implemented on the MIAS medical dataset using 200 mammogram breast images. Compared to similar approaches based on KNN and RF, the proposed approaches show better performance in terms of accuracy, sensitivity, spasticity, precision, recall, time of performance, and quality of image metrics.

show abstract

Section: B Taxonomy Of Ai-based Approaches For Breast Cancer Diagnosismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Enhanced Artificial Intelligence System for Diagnosing and Predicting Breast Cancer using Deep Learning

Alfifi¹,

Alrahhal²,

Bataineh³

et al. 2020

IJACSA

View full text Add to dashboard Cite

show abstract

“…In 2018, Al‐antari et al proposed a deep learning‐based method for the automatic diagnosis of breast cancer in digital mammograms . The method was based on a deep belief network (DBN) that automatically detects breast tissue areas and detects the benign and malignant regions.…”

Section: Introductionmentioning

confidence: 99%

Deep learning and optimization algorithms for automatic breast cancer detection

Sha

Hu²,

Rouyendegh

2020

Int J Imaging Syst Tech

View full text Add to dashboard Cite

Breast cancer is caused by the abnormal and rapid growth of breast cells. An early diagnosis can ensure an easier and effective treatment. A mass in the breast is a significant early sign of breast cancer, even though differentiating the cancerous mass's tissue from normal tissue for diagnosis is a difficult task for radiologists. The development of computer-aided detection systems in recent years has led to nondestructive and efficient cancer diagnostic techniques. This paper proposes a comprehensive method to locate the cancerous region in the mammogram image. This method employs image noise reduction, optimal image segmentation based on the convolutional neural network, a grasshopper optimization algorithm, and optimized feature extraction and feature selection based on the grasshopper optimization algorithm, thereby improving precision and decreasing the computational cost. This method was applied to the Mammographic Image Analysis Society Digital Mammogram Database and Digital Database for Screening Mammography breast cancer databases and the simulation results were compared with 10 different state-ofthe-art methods to analyze the proposed system's efficiency. Final results showed that the proposed method had 96% Sensitivity, 93% Specificity, 85% PPV, 97% NPV, 92% accuracy, and better efficiency than other traditional methods in terms of Sensitivity, Specificity, PPV, NPV, and Accuracy. K E Y W O R D S breast cancer, convolutional neural networks, feature extraction, feature selection, grasshopper optimization algorithm, image classification, image segmentation

show abstract

“…Mammography has been regarded as the preferred method of breast cancer, due to its clear imaging and sensitivity to early breast masses [5]. Also, the use of CNN-based methods to classify the benign and malignant (BM) of breast masses in mammography has important clinical significance and practical application value [6]- [8]. At present, when traditional manual radiograph readings are used to classify the BM of breast masses, the professional knowledge and clinical experience of different doctor is different, resulting in poor reproducibility of diagnostic results.…”

Section: Introductionmentioning

confidence: 99%

Multi-Tasking U-Shaped Network for Benign and Malignant Classification of Breast Masses

Cao¹,

Pu²,

Tan³

et al. 2020

IEEE Access

View full text Add to dashboard Cite

An Automatic Computer-Aided Diagnosis System for Breast Cancer in Digital Mammograms via Deep Belief Network

Cited by 107 publications

References 33 publications

Enhanced Artificial Intelligence System for Diagnosing and Predicting Breast Cancer using Deep Learning

Enhanced Artificial Intelligence System for Diagnosing and Predicting Breast Cancer using Deep Learning

Deep learning and optimization algorithms for automatic breast cancer detection

Multi-Tasking U-Shaped Network for Benign and Malignant Classification of Breast Masses

Contact Info

Product

Resources

About