A powerful Transfer learning technique for multiclass classification of lung cancer CT images
Author:
Affiliation:
1. National Institute of Technology Silchar,Bio-Medical Imaging Laboratory (BIOMIL),Dept. of ECE,Silchar,Assam,India,788010
Funder
Science and Engineering Research Board
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10007063/10006838/10007294.pdf?arnumber=10007294
Reference24 articles.
1. Learning Lung Nodule Malignancy Likelihood from Radiologist Annotations or Diagnosis Data
2. The iq-oth/nccd lung cancer dataset, version 1, retrieved on 14th november, 2021;kareem,0
3. Superpixel with nanoscale imaging and boosted deep convolutional neural network concept for lung tumor classification
4. A Transfer Learning Architecture Based on a Support Vector Machine for Histopathology Image Classification
5. Comprehensive and Comparative Global and Local Feature Extraction Framework for Lung Cancer Detection Using CT Scan Images
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1. CT Image-Based Lung Cancer Detection Using a CNN-Based Model;2023 4th International Conference on Data Analytics for Business and Industry (ICDABI);2023-10-25
2. A multi-class deep learning model for early lung cancer and chronic kidney disease detection using computed tomography images;Frontiers in Oncology;2023-06-02
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