GC-EnC: A Copula based ensemble of CNNs for malignancy identification in breast histopathology and cytology images

Dey, Soumyajyoti; Mitra, Shyamali; Chakraborty, Sukanta; Mondal, Debashri; Nasipuri, Mita; Das, Nibaran

doi:10.1016/j.compbiomed.2022.106329

Cited by 8 publications

(1 citation statement)

References 42 publications

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“…Glioma detection [39] COVID-19 detection [40][41][42][43] Breast cancer [44][45][46][47][48] Colorectal polyp classification [49] Brain cancer detection [50] Lymphoblastic leukemia cell image analysis [51] Alzheimer's disease classification [52] Monkeypox detection [53] https://doi.org/10.1371/journal.pone.0298527.t001 InceptionResNetV2. Introduced by [59], InceptionResNetV2 combines the strengths of Inception and ResNet architectures.…”

Section: Application Domain Referencementioning

confidence: 99%

MENet: A Mitscherlich function based ensemble of CNN models to classify lung cancer using CT scans

Majumder,

Gautam,

Basu

et al. 2024

PLoS ONE

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Lung cancer is one of the leading causes of cancer-related deaths worldwide. To reduce the mortality rate, early detection and proper treatment should be ensured. Computer-aided diagnosis methods analyze different modalities of medical images to increase diagnostic precision. In this paper, we propose an ensemble model, called the Mitscherlich function-based Ensemble Network (MENet), which combines the prediction probabilities obtained from three deep learning models, namely Xception, InceptionResNetV2, and MobileNetV2, to improve the accuracy of a lung cancer prediction model. The ensemble approach is based on the Mitscherlich function, which produces a fuzzy rank to combine the outputs of the said base classifiers. The proposed method is trained and tested on the two publicly available lung cancer datasets, namely Iraq-Oncology Teaching Hospital/National Center for Cancer Diseases (IQ-OTH/NCCD) and LIDC-IDRI, both of these are computed tomography (CT) scan datasets. The obtained results in terms of some standard metrics show that the proposed method performs better than state-of-the-art methods. The codes for the proposed work are available at https://github.com/SuryaMajumder/MENet.

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