Deep learning algorithm for breast masses classification in mammograms

Gnanasekaran, Vaira Suganthi; Joypaul, Sutha; Sundaram, Parvathy Meenakshi; Devi, D.

doi:10.1049/iet-ipr.2020.0070

Cited by 51 publications

(25 citation statements)

References 25 publications

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“…In this paper [ 23 ], the author suggested a model for the classification of breast masses using the CAD method. MIAS, self-collected datasets, and DDSM datasets are used in this study.…”

Section: Related Workmentioning

confidence: 99%

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Breast Tumor Detection and Classification in Mammogram Images Using Modified YOLOv5 Network

Mohiyuddin

Basharat

Ghani

et al. 2022

Computational and Mathematical Methods in Medicine

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Breast cancer incidence has been rising steadily during the past few decades. It is the second leading cause of death in women. If it is diagnosed early, there is a good possibility of recovery. Mammography is proven to be an excellent screening technique for breast tumor diagnosis, but its detection and classification in mammograms remain a significant challenge. Previous studies’ major limitation is an increase in false positive ratio (FPR) and false negative ratio (FNR), as well as a drop in Matthews correlation coefficient (MCC) value. A model that can lower FPR and FNR while increasing MCC value is required. To overcome prior research limitations, a modified network of YOLOv5 is used in this study to detect and classify breast tumors. Our research is conducted using publicly available datasets Curated Breast Imaging Subset of DDSM (CBIS-DDSM). The first step is to perform preprocessing, which includes image enhancing techniques and the removal of pectoral muscles and labels. The dataset is then annotated, augmented, and divided into 60% for training, 30% for validation, and 10% for testing. The experiment is then performed using a batch size of 8, a learning rate of 0.01, a momentum of 0.843, and an epoch value of 300. To evaluate the performance of our proposed model, our proposed model is compared with YOLOv3 and faster RCNN. The results show that our proposed model performs better than YOLOv3 and faster RCNN with 96% mAP, 93.50% MCC value, 96.50% accuracy, 0.04 FPR, and 0.03 FNR value. The results show that our suggested model successfully identifies and classifies breast tumors while also overcoming previous research limitations by lowering the FPR and FNR and boosting the MCC value.

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“…In this paper [ 23 ], the author suggested a model for the classification of breast masses using the CAD method. MIAS, self-collected datasets, and DDSM datasets are used in this study.…”

Section: Related Workmentioning

confidence: 99%

“…In this paper [ 23 ], an author has suggested a method to detect breast cancer from mammograms. Preprocessing, segmentation, extraction of features, and classification are used in this research.…”

Section: Related Workmentioning

confidence: 99%

Breast Tumor Detection and Classification in Mammogram Images Using Modified YOLOv5 Network

Mohiyuddin

Basharat

Ghani

et al. 2022

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

show abstract

“…The Faster-RCNN was developed [ 24 ] for regional classification in order to increase the sensitivity of mass detection, and therefore the rate of detection, from mammograms for the prediction of breast cancer. Reference [ 25 ] developed an approach based on a deep learning algorithm for the classification of breast masses and achieved an accuracy of 96.47 by evaluating the approach on public datasets. An extreme learning approach was employed to map the feature fusion to obtain the CNN features for breast cancer detection and classification by Z. Wang et al [ 26 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Computer Vision-Based Microcalcification Detection in Digital Mammograms Using Fully Connected Depthwise Separable Convolutional Neural Network

Rehman

Pei

et al. 2021

Sensors

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Microcalcification clusters in mammograms are one of the major signs of breast cancer. However, the detection of microcalcifications from mammograms is a challenging task for radiologists due to their tiny size and scattered location inside a denser breast composition. Automatic CAD systems need to predict breast cancer at the early stages to support clinical work. The intercluster gap, noise between individual MCs, and individual object’s location can affect the classification performance, which may reduce the true-positive rate. In this study, we propose a computer-vision-based FC-DSCNN CAD system for the detection of microcalcification clusters from mammograms and classification into malignant and benign classes. The computer vision method automatically controls the noise and background color contrast and directly detects the MC object from mammograms, which increases the classification performance of the neural network. The breast cancer classification framework has four steps: image preprocessing and augmentation, RGB to grayscale channel transformation, microcalcification region segmentation, and MC ROI classification using FC-DSCNN to predict malignant and benign cases. The proposed method was evaluated on 3568 DDSM and 2885 PINUM mammogram images with automatic feature extraction, obtaining a score of 0.97 with a 2.35 and 0.99 true-positive ratio with 2.45 false positives per image, respectively. Experimental results demonstrated that the performance of the proposed method remains higher than the traditional and previous approaches.

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“…Gnana S. et al [ 33 ] developed a computer-aided diagnostic system using a deep convolutional neural network to classify malignant and benign masses. A deep active and self-paced learning-based framework was emphasized for detecting breast mass from digital mammograms by Shen et al [ 34 ] to reduce the annotation effort for radiologists.…”

Section: Related Work and Techniquesmentioning

confidence: 99%

Architectural Distortion-Based Digital Mammograms Classification Using Depth Wise Convolutional Neural Network

Rehman

Pei

et al. 2021

Biology

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Architectural distortion is the third most suspicious appearance on a mammogram representing abnormal regions. Architectural distortion (AD) detection from mammograms is challenging due to its subtle and varying asymmetry on breast mass and small size. Automatic detection of abnormal ADs regions in mammograms using computer algorithms at initial stages could help radiologists and doctors. The architectural distortion star shapes ROIs detection, noise removal, and object location, affecting the classification performance, reducing accuracy. The computer vision-based technique automatically removes the noise and detects the location of objects from varying patterns. The current study investigated the gap to detect architectural distortion ROIs (region of interest) from mammograms using computer vision techniques. Proposed an automated computer-aided diagnostic system based on architectural distortion using computer vision and deep learning to predict breast cancer from digital mammograms. The proposed mammogram classification framework pertains to four steps such as image preprocessing, augmentation and image pixel-wise segmentation. Architectural distortion ROI`s detection, training deep learning, and machine learning networks to classify AD`s ROIs into malignant and benign classes. The proposed method has been evaluated on three databases, the PINUM, the CBIS-DDSM, and the DDSM mammogram images, using computer vision and depth-wise 2D V-net 64 convolutional neural networks and achieved 0.95, 0.97, and 0.98 accuracies, respectively. Experimental results reveal that our proposed method outperforms as compared with the ShuffelNet, MobileNet, SVM, K-NN, RF, and previous studies.

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Deep learning algorithm for breast masses classification in mammograms

Cited by 51 publications

References 25 publications

Breast Tumor Detection and Classification in Mammogram Images Using Modified YOLOv5 Network

Breast Tumor Detection and Classification in Mammogram Images Using Modified YOLOv5 Network

Computer Vision-Based Microcalcification Detection in Digital Mammograms Using Fully Connected Depthwise Separable Convolutional Neural Network

Architectural Distortion-Based Digital Mammograms Classification Using Depth Wise Convolutional Neural Network

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