Fuzzy ensemble of deep learning models using choquet fuzzy integral, coalition game and information theory for breast cancer histology classification

Bhowal, Pratik; Sen, Subhankar; Velásquez, Juan D.; Sarkar, Ram

doi:10.1016/j.eswa.2021.116167

Cited by 42 publications

(9 citation statements)

References 31 publications

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“…The notable contribution of the work is that the authors reduced the problem complexity using a down-sampling technique where the image size was reduced by a factor of k. It also used the central patch of size m * m as input to the model which reduced the training complexity of the model further. Recently, Bhowal et al [28] proposed a classifier combination method that used Choquet fuzzy integral as the aggregator function. The aggregator was used to combine the confidence scores returned by the CNN based classifiers.…”

Section: Pathological Image Based Methodsmentioning

confidence: 99%

“…The authors used three static features: taxonomic indices, statistical measures and LBP along with CNN based features. Chattopadhyay et al [38] designed a DL based method called Dense Residual Dual-shuffle Attention MILCNN* 0.92 -----Shallu and Mehra [199] VGG 16 + LR 0.92 0.93 0.93 0.93 -0.95 Jannesari et al [105] ResNet V1 152 0.98 0.99 ---0.98 Gupta and Chawla [77] ResNet50+LR 0.93 -----Chattopadhyay et al [38] DRDA-Net 0.98 -----BACH Dataset Rakhlin et al [172] LightGBM + CNN 0.87 -----Yang et al [230] EMS-Net 0.91 -----Roy et al [178] Self-designed (OPOD) 0.77 0.77 -0.77 0.77 -Roy et al [178] Self-designed (APOD) 0.90 0.92 -0.90 0.90 -Sanyal et al [189] Hybrid Ensemble (OPOD) 0.87 0.86 0.87 0.86 0.99 -Sanyal et al [189] Hybrid Ensemble (APOD) 0.95 0.95 0.95 0.95 0.98 -Bhowal et al [28] Choquet fuzzy integral and coalition game based classifier ensemble 0.95 -----…”

Section: Pathological Image Based Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Computer Based Diagnosis of Some Chronic Diseases: A Medical Journey of the Last Two Decades

Malakar¹,

Roy

Das

et al. 2022

Arch Computat Methods Eng

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Disease prediction from diagnostic reports and pathological images using artificial intelligence (AI) and machine learning (ML) is one of the fastest emerging applications in recent days. Researchers are striving to achieve near-perfect results using advanced hardware technologies in amalgamation with AI and ML based approaches. As a result, a large number of AI and ML based methods are found in the literature. A systematic survey describing the state-of-the-art disease prediction methods, specifically chronic disease prediction algorithms, will provide a clear idea about the recent models developed in this field. This will also help the researchers to identify the research gaps present there. To this end, this paper looks over the approaches in the literature designed for predicting chronic diseases like Breast Cancer, Lung Cancer, Leukemia, Heart Disease, Diabetes, Chronic Kidney Disease and Liver Disease. The advantages and disadvantages of various techniques are thoroughly explained. This paper also presents a detailed performance comparison of different methods. Finally, it concludes the survey by highlighting some future research directions in this field that can be addressed through the forthcoming research attempts.

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Section: Pathological Image Based Methodsmentioning

confidence: 99%

Section: Pathological Image Based Methodsmentioning

confidence: 99%

Computer Based Diagnosis of Some Chronic Diseases: A Medical Journey of the Last Two Decades

Malakar¹,

Roy

Das

et al. 2022

Arch Computat Methods Eng

Self Cite

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“…Artificial intelligence (AI) technologies may offer solutions to this and other issues (8,9). AI models trained to interpret digital pathology imaging have been shown to predict specific clinical events, such as treatment response, prognosis assessment, classification, grading or scoring of different cancers (10)(11)(12)(13)(14)(15) and detection of lymph node metastases (16). Takamatsu used Image J, a deep learning (DL) algorithm, to predict risk of preoperative LNM from pathological images of T1 colorectal cancer by extracting morphologic parameters from whole-slide images (WSI).…”

Section: Introductionmentioning

confidence: 99%

A deep learning model for lymph node metastasis prediction based on digital histopathological images of primary endometrial cancer

Feng¹,

Zhao²,

Chen³

et al. 2023

Quant Imaging Med Surg

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Background: The current study aimed to develop a deep learning (DL) model for prediction of lymph node metastasis (LNM) based on hematoxylin and eosin (HE)-stained histopathological images of endometrial cancer (EC). The model was validated using external data.Methods: A total of 2,104 whole slide image (WSI) from 564 patients with pathologically confirmed LNM status were collated from West China Second University Hospital. An artificial intelligence (AI) model was built on the multiple instance-learning (MIL) framework for automatic prediction of the probability of LNM and its performance compared with "Mayo criteria". An additional external data source comprising 533 WSI was collected from two independent medical institutions to validate the model's robustness. Heatmaps were generated to demonstrate regions of the WSI that made the greatest contributions to the DL network output to improve understanding of these processes. Results:The proposed MIL model achieved an area under the curve (AUC) of 0.938, a sensitivity of 0.830 and a specificity of 0.911 for LNM prediction to EC. The AUC according to Mayo criteria was 0.666 for the same test dataset. For types I, II and mixed EC, AUCs were 0.927, 0.979 and 0.929, respectively. The predictive performance of the MIL model also achieved an AUC of 0.921 for early staging. In external validation data, the proposed model achieved an AUC of 0.770, a sensitivity of 0.814 and a specificity of 0.520 for LNM prediction. AUCs were 0.783 for type I and 0.818 for early stage EC. Conclusions:The proposed MIL model generated from histopathological images of EC has a much better LNM predictive performance than that of Mayo criteria. A novel DL-based biomarker trained on different histological subtypes of EC slides was revealed to predict metastatic status with improved accuracy, especially for early staging patients. The current study proves the concept of MIL-based prediction of LNM in EC for the first time, and brought a new sight to improve the accuracy of LNM prediction. Multicenter prospective validation data is required to further confirm the clinical utility.

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“…Thuy et al [ 14 ] used a hybrid deep learning model that incorporated VGG19 and VGG16 models, as well as a generative adversarial network (GAN) to improve classification performance and reached 98.1% accuracy. Bhowal et al [ 15 ] used the Coalition Game and Information Theory to present Choquet Integral-based deep CNN models for a four-class problem in breast cancer histology and achieved 95% accuracy. Khan et al [ 16 ] recommended a novel CNN model combined with various transfer learning algorithms and achieved 97.67% accuracy.…”

Section: Introductionmentioning

confidence: 99%

Breast Cancer Detection in Mammography Images Using Deep Convolutional Neural Networks and Fuzzy Ensemble Modeling Techniques

et al. 2022

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Breast cancer has evolved as the most lethal illness impacting women all over the globe. Breast cancer may be detected early, which reduces mortality and increases the chances of a full recovery. Researchers all around the world are working on breast cancer screening tools based on medical imaging. Deep learning approaches have piqued the attention of many in the medical imaging field due to their rapid growth. In this research, mammography pictures were utilized to detect breast cancer. We have used four mammography imaging datasets with a similar number of 1145 normal, benign, and malignant pictures using various deep CNN (Inception V4, ResNet-164, VGG-11, and DenseNet121) models as base classifiers. The proposed technique employs an ensemble approach in which the Gompertz function is used to build fuzzy rankings of the base classification techniques, and the decision scores of the base models are adaptively combined to construct final predictions. The proposed fuzzy ensemble techniques outperform each individual transfer learning methodology as well as multiple advanced ensemble strategies (Weighted Average, Sugeno Integral) with reference to prediction and accuracy. The suggested Inception V4 ensemble model with fuzzy rank based Gompertz function has a 99.32% accuracy rate. We believe that the suggested approach will be of tremendous value to healthcare practitioners in identifying breast cancer patients early on, perhaps leading to an immediate diagnosis.

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Fuzzy ensemble of deep learning models using choquet fuzzy integral, coalition game and information theory for breast cancer histology classification

Cited by 42 publications

References 31 publications

Computer Based Diagnosis of Some Chronic Diseases: A Medical Journey of the Last Two Decades

Computer Based Diagnosis of Some Chronic Diseases: A Medical Journey of the Last Two Decades

A deep learning model for lymph node metastasis prediction based on digital histopathological images of primary endometrial cancer

Breast Cancer Detection in Mammography Images Using Deep Convolutional Neural Networks and Fuzzy Ensemble Modeling Techniques

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