TY - JOUR
T1 - GARL-Net
T2 - Graph Based Adaptive Regularized Learning Deep Network for Breast Cancer Classification
AU - Patel, Vivek
AU - Chaurasia, Vijayshri
AU - Mahadeva, Rajesh
AU - Patole, Shashikant P.
N1 - Funding Information:
This work was supported by the Khalifa University of Science and Technology under Khalifa University Research Internal Fund.
Publisher Copyright:
© 2013 IEEE.
PY - 2023
Y1 - 2023
N2 - Across the globe, women suffer from breast cancer fatal disease. It is arising surprisingly due to a lack of awareness among them and the inconvenient reach of diagnostic systems. Many computer-aided diagnostic systems have been developed for the detection of cancer. These systems have quite lower performance, so more accurate diagnosis is the need of the time to save the life of human beings. For large and imbalanced image datasets, efficient learning of the network is very important to detect and classify breast cancer more accurately. In this paper, a graph based adaptive regularized learning of deep network (GARL-Net) is proposed for more accurate breast cancer classification. Transfer learning is used for training the backbone network DenseNet121. furthermore, fine tuning of backbone network is followed by the estimated improved loss function. The improved loss function is actually graph based adaptively regularized complement cross entropy loss. The SoftMax cross entropy in itself is not sufficient to classify image samples accurately, so complement entropy technique is incorporated with the cross-entropy loss to overcome the misclassification issue. Further, by adaptive scaling of regularization term with spatial graph Laplacian basis used to adaptively penalize the complement cross entropy loss for improving the learning of the network. The performance of the proposed method is evaluated using BreakHis and BACH 2018 histopathology image datasets and outperforms the existing state-of-the-art methods and achieved 99.00% of precision, 99.40% of recall, 99.20 % of F-1score, and 99.49% of accuracy for binary classification of breast cancer image samples of BreakHis dataset.
AB - Across the globe, women suffer from breast cancer fatal disease. It is arising surprisingly due to a lack of awareness among them and the inconvenient reach of diagnostic systems. Many computer-aided diagnostic systems have been developed for the detection of cancer. These systems have quite lower performance, so more accurate diagnosis is the need of the time to save the life of human beings. For large and imbalanced image datasets, efficient learning of the network is very important to detect and classify breast cancer more accurately. In this paper, a graph based adaptive regularized learning of deep network (GARL-Net) is proposed for more accurate breast cancer classification. Transfer learning is used for training the backbone network DenseNet121. furthermore, fine tuning of backbone network is followed by the estimated improved loss function. The improved loss function is actually graph based adaptively regularized complement cross entropy loss. The SoftMax cross entropy in itself is not sufficient to classify image samples accurately, so complement entropy technique is incorporated with the cross-entropy loss to overcome the misclassification issue. Further, by adaptive scaling of regularization term with spatial graph Laplacian basis used to adaptively penalize the complement cross entropy loss for improving the learning of the network. The performance of the proposed method is evaluated using BreakHis and BACH 2018 histopathology image datasets and outperforms the existing state-of-the-art methods and achieved 99.00% of precision, 99.40% of recall, 99.20 % of F-1score, and 99.49% of accuracy for binary classification of breast cancer image samples of BreakHis dataset.
KW - breast cancer image classification
KW - complement cross entropy
KW - computer aided diagnostic
KW - Deep learning
KW - graph Laplacian basis regularization
KW - transfer learning
UR - http://www.scopus.com/inward/record.url?scp=85147270075&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2023.3239671
DO - 10.1109/ACCESS.2023.3239671
M3 - Article
AN - SCOPUS:85147270075
SN - 2169-3536
VL - 11
SP - 9095
EP - 9112
JO - IEEE Access
JF - IEEE Access
ER -