Towards Automated Breast Mass Classification using Deep Learning Framework

Sarkar, Pinaki Ranjan; Prabhakar, Priya; Mishra, Deepak; Subrahmanyam, Gorthi R. K. Sai

doi:10.1109/dsaa.2019.00060

Cited by 7 publications

(1 citation statement)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thus, many studies have integrated the rst stage of identifying the suspicious region of breast lesions and based on its automated output, performed the segmentation and classi cation tasks. For instance, Sarkar et al [39] proposed an automated CAD system that detects suspicious regions of potential lesions using a deep hierarchical prediction network and then classi es them into mass or non-mass, and nally into malignant or benign using a CNN structure. The work was tested and achieved an accuracy of 98.05% on the DDSM dataset and 98.14% on the INbreast dataset.…”

Section: Technical Backgroundmentioning

confidence: 99%

An integrated Framework for Breast Mass Classification and Diagnosis using Stacked Ensemble of Residual Neural Networks

Baccouche

Garcia‐Zapirain

Elmaghraby

2022

Preprint

View full text Add to dashboard Cite

A computer-aided diagnosis (CAD) system requires automated stages of tumor detection, segmentation, and classification that are integrated sequentially in one framework to assist the radiologists with a final diagnosis decision. In this paper, we introduce the final step of breast mass classification and diagnosis using a stacked ensemble of residual neural network (ResNet) models (i.e. ResNet50V2, ResNet101V2, and ResNet152V2). The work presents the task of classifying the detected and segmented breast masses into malignant or benign, and diagnosing the Breast Imaging Reporting and Data System (BI-RADS) assessment category with a score from 2 to 6 and the shape as oval, round, lobulated, or irregular. The proposed methodology was evaluated on two publicly available datasets, the Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM) and INbreast, and additionally on a private dataset. Comparative experiments were conducted on the individual models and an average ensemble of models with an XGBoost classifier. Qualitative and quantitative results show that the proposed model achieved better performance for 1) Pathology classification with an accuracy of 95.13%, 99.20%, and 95.88%; 2) BI-RADS category classification with an accuracy of 85.38%, 99%, and 96.08% respectively on CBIS-DDSM, INbreast, and the private dataset; and 3) shape classification with 90.02% on the CBIS-DDSM dataset. Our results demonstrate that our proposed integrated framework could benefit from all automated stages to outperform the latest deep learning methodologies.

show abstract

Section: Technical Backgroundmentioning

confidence: 99%

An integrated Framework for Breast Mass Classification and Diagnosis using Stacked Ensemble of Residual Neural Networks

Baccouche

Garcia‐Zapirain

Elmaghraby

2022

Preprint

View full text Add to dashboard Cite

show abstract

Adaptive channel and multiscale spatial context network for breast mass segmentation in full-field mammograms

Zhao

Lou

et al. 2021

Appl Intell

View full text Add to dashboard Cite

Fully convolutional network for automated detection and diagnosis of mammographic masses

Kulkarni

Rabidas

2023

Multimed Tools Appl

View full text Add to dashboard Cite

Breast cancer, though rare in male, is very frequent in female and has high mortality rate which can be reduced if detected and diagnosed at the early stage. Thus, in this paper, deep learning architecture based on U-Net is proposed for the detection of breast masses and its characterization as benign or malignant. The evaluation of the proposed architecture in detection is carried out on two benchmark datasets— INbreast and DDSM and achieved a true positive rate of 99.64% at 0.25 false positives per image for INbreast dataset while the same for DDSM are 97.36% and 0.38 FPs/I, respectively. For mass characterization, an accuracy of 97.39% with an AUC of 0.97 is obtained for INbreast while the same for DDSM are 96.81%, and 0.96, respectively. The measured results are further compared with the state-of-the-art techniques where the introduced scheme takes an edge over others.

show abstract

Towards Automated Breast Mass Classification using Deep Learning Framework

Cited by 7 publications

References 28 publications

An integrated Framework for Breast Mass Classification and Diagnosis using Stacked Ensemble of Residual Neural Networks

An integrated Framework for Breast Mass Classification and Diagnosis using Stacked Ensemble of Residual Neural Networks

Adaptive channel and multiscale spatial context network for breast mass segmentation in full-field mammograms

Fully convolutional network for automated detection and diagnosis of mammographic masses

Contact Info

Product

Resources

About