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

Surya Majumder, Nandita Gautam, Abhishek Basu, Arup Sau, Zong Woo Geem and Ram Sarkar

PLOS ONE, 2024, vol. 19, issue 3, 1-29

Abstract: 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.

Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0298527

DOI: 10.1371/journal.pone.0298527

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