Faculty Publications

Now showing 1 - 3 of 3

A Novel Decision Level Class-Wise Ensemble Method in Deep Learning for Automatic Multi-Class Classification of HER2 Breast Cancer Hematoxylin-Eosin Images
(Institute of Electrical and Electronics Engineers Inc., 2024) Pateel, G.P.; Senapati, K.; Pandey, A.K.
The Human Epidermal Growth Factor Receptor 2 (HER2) is one of the aggressive subtypes of breast cancer. The HER2 status decides the requirement of breast cancer patients to receive HER2-targeted therapy. The HER2 testing involves combining Immunohistochemistry (IHC) screening, followed by fluorescence in situ hybridization (FISH) for cases where IHC results are equivocal. These tests may involve multiple trials, are time intensive, and tend to be more expensive for certain classes of people. Hematoxylin and Eosin (HE) staining is employed for visualizing general tissue morphology and is a routine, cost-effective method. In this study, we introduce a novel automated class-wise weighted average ensemble deep learning algorithm at the decision level. The proposed algorithm fuses three pre-trained deep-learning models at the decision level by assigning a weight to each class based on their performance of the model to classify the HE-stained breast histopathology images into multi-class HER2 statuses as HER2-0+, HER2-1+, HER2-2+, and HER2-3+. The class-wise weight allocation to the base classifiers is one of the key features of the proposed algorithm. The presented framework surpasses all the existing methods currently employed on the Breast Cancer Immunohistochemistry (BCI) dataset, establishing itself as a dependable approach for detecting HER2 status from HE-stained images. This study highlights the robustness of the proposed algorithm as well as the sufficient information encapsulated within HE-stained images for the effective detection of the HER2 protein present in breast cancer. Therefore, the proposed method possesses the potential to sideline the need for IHC laboratory tests, which hoard time and money. © 2013 IEEE.
Improving the performance of multi-stage HER2 breast cancer detection in hematoxylin-eosin images based on ensemble deep learning
(Elsevier Ltd, 2025) Pateel, G.P.; Senapati, K.; Pandey, A.K.
Background: Breast cancer is the most frequently diagnosed cancer among women worldwide, and histopathology is the gold standard in diagnosing the disease. Hematoxylin and Eosin (HE) staining, routinely employed to observe the overall tissue structure, is an affordable and commonly practiced cancer diagnosis. In contrast, Immunohistochemistry (IHC), which detects the increased presence of particular antigens linked to the mutation, can require multiple tests to conduct and is relatively costly. Generally, in computer-aided diagnosis, the conventional methods rely on a single network to extract features. However, these methods have significant limitations and fail to generalize. Methods: In this study, we propose an automated novel weighted average algorithm called HER2-ETNET, which ensembles the chosen three pre-trained deep learning models, DenseNet 201, GoogLeNet, and ResNet-50, to classify breast histopathology HE images into multi-class Human Epidermal Growth Factor Receptor-2 (HER2) status (HER2-0+, HER2-1+, HER2-2+, HER2-3+). The proposed method has the potential to bypass the IHC laboratory test. In this study, we form a weight matrix by fusing together, the scores of False Positive Rate (FPR) and False Negative Rate (FNR) of both training and validation sets, and the computed weights are assigned to the three base learners. This is in contrast to the previous works, in which the weights were generally assigned empirically to the chosen deep learning models, which might be erroneous. Result: The proposed approach is evaluated on the unseen test set, and it achieves accuracy, precision, recall and AUC of 97.44%, 97.32%, 97.39%, and 99.75% respectively. Conclusion: The proposed framework outperforms all the existing methods on the same dataset and is proven to be the reliable method in detecting the HER2 status (HER2-0+, HER2-1+, HER2-2+, HER2-3+) from HE images. This also proves that, HE stained images contain adequate information for efficiently detecting the HER2 status in breast cancer. © 2024 Elsevier Ltd
Improving Vertebral Fracture Detection in C-Spine CT Images Using Bayesian Probability-Based Ensemble Learning
(Multidisciplinary Digital Publishing Institute (MDPI), 2025) Pandey, A.K.; Senapati, K.; Argyros, I.K.; Pateel, G.P.
Vertebral fracture (VF) may induce spinal cord injury that can lead to serious consequences which eventually may paralyze the entire or some parts of the body depending on the location and severity of the injury. Diagnosis of VFs is crucial at the initial stage, which may be challenging because of the subtle features, noise, and homogeneity present in the computed tomography (CT) images. In this study, Wide ResNet-40, DenseNet-121, and EfficientNet-B7 are chosen, fine-tuned, and used as base models, and a Bayesian-based probabilistic ensemble learning method is proposed for fracture detection in cervical spine CT images. The proposed method considers the prediction’s uncertainty of the base models and combines the predictions obtained from them, to improve the overall performance significantly. This method assigns weights to the base learners, based on their performance and confidence about the prediction. To increase the robustness of the proposed model, custom data augmentation techniques are performed in the preprocessing step. This work utilizes 15,123 CT images from the RSNA-2022 C-spine fracture detection challenge and demonstrates superior performance compared to the individual base learners, and the other existing conventional ensemble methods. The proposed model also outperforms the best state-of-the-art (SOTA) model by 1.62%, 0.51%, and 1.29% in terms of accuracy, specificity, and sensitivity, respectively; furthermore, the AUC score of the best SOTA model is lagging by 5%. The overall accuracy, specificity, sensitivity, and F1-score of the proposed model are 94.62%, 93.51%, 95.29%, and 93.16%, respectively. © 2025 by the authors.

Faculty Publications

Browse

Filters

Settings

Sort By

Results per page

Search Results