MENet: A Mitscherlich function based ensemble of CNN models to classify lung cancer using CT scansopen access
- Authors
- Majumder, Surya; Gautam, Nandita; Basu, Abhishek; Sau, Arup; Geem, Zong Woo; Sarkar, Ram
- Issue Date
- Mar-2024
- Publisher
- PUBLIC LIBRARY SCIENCE
- Citation
- PLOS ONE, v.19, no.3
- Journal Title
- PLOS ONE
- Volume
- 19
- Number
- 3
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/91133
- DOI
- 10.1371/journal.pone.0298527
- ISSN
- 1932-6203
- 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.
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