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MENet: A Mitscherlich function based ensemble of CNN models to classify lung cancer using CT scans.
Majumder, Surya; Gautam, Nandita; Basu, Abhishek; Sau, Arup; Geem, Zong Woo; Sarkar, Ram.
Afiliación
  • Majumder S; Department of Computer Science and Engineering, Heritage Institute of Technology, Kolkata, India.
  • Gautam N; Department of Computer Science and Engineering, Jadavpur University, Kolkata, India.
  • Basu A; Department of Computer Science and Engineering, Jadavpur University, Kolkata, India.
  • Sau A; Department of Computer Science and Engineering, National Institute of Technology Durgapur, Durgapur, India.
  • Geem ZW; Department of Computer Science and Engineering, Jadavpur University, Kolkata, India.
  • Sarkar R; College of IT Convergence, Gachon University, Seongnam, South Korea.
PLoS One ; 19(3): e0298527, 2024.
Article en En | MEDLINE | ID: mdl-38466701
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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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias Pulmonares Límite: Humans País/Región como asunto: Asia Idioma: En Revista: PLoS One Asunto de la revista: CIENCIA / MEDICINA Año: 2024 Tipo del documento: Article País de afiliación: India Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias Pulmonares Límite: Humans País/Región como asunto: Asia Idioma: En Revista: PLoS One Asunto de la revista: CIENCIA / MEDICINA Año: 2024 Tipo del documento: Article País de afiliación: India Pais de publicación: Estados Unidos