An integrated framework for breast mass classification and diagnosis using stacked ensemble of residual neural networks

Abstract

A computer-aided diagnosis (CAD) system requires automated stages of tumor detection, segmentation, and classification that are integrated sequentially into 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.

BC2NetRF: Breast Cancer Classification from Mammogram Images Using Enhanced Deep Learning Features and Equilibrium-Jaya Controlled Regula Falsi-Based Features Selection

“…Houbey et al [ 43 ] obtained an improved accuracy of 96.52% on the INbreast dataset. In [ 44 ], the authors obtained 85.38% and 99% accuracy for the CBIS-DDSM and INbreast datasets. This work achieves an accuracy of 95.4% for CBIS-DDSM and 99.7% for the INbreast cancer dataset, which shows an improvement.…”

Breast Cancer Analytics Classification using MEFBUD DCNN Techniques

“…The suggested framework works better than current methods when evaluated to them. Subasish Mohapatra [2022] [14] analysed the efficiency of numerous Architectures, including AlexNet, VGG16, and ResNet50, by creating sections of them from scratch and utilising transfer learning with which was before values on others. Using mammography pictures from the mini-DDSM data, which is freely accessible, the aforementioned system classifications were evaluated and trained.…”

Breast Cancer Classification Using Concatenated Triple Convolutional Neural Networks Model

“…CNN model for detection of architectural distortion [43] 87.50% lightweight end-to-end improved CNN [44] 97.20% VGG16 [44] 95.04% SVM [44] 92.23% PCA + SVM [44] 90.59% Deep Vision supervised learning [45] 97% CNN Model with Transfer learning in binary classification [46] 92% CNN Model with Transfer learning in multi-class classification [46] 85% Haze-reduced local-global + EfficientNet-b0 [47] 95.4% Stacked ensemble of residual neural networks [48] 85.39% CNN with less learnable parameters [49] 90.68% Case-based reasoning system [50] 86.71% DL feature fusion + satin bowerbird optimization-controlled Newton Raphson feature selection [51] 94.5% Proposed model in binary classification 97.26% Proposed model in multi-classification 99.13%…”