| ORIGINAL ARTICLE |
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| Year : 2022 | Volume
: 47
| Issue : 1 | Page : 1-9 |
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A novel hybridized feature extraction approach for lung nodule classification based on transfer learning technique
P Malin Bruntha1, S Immanuel Alex Pandian1, J Anitha2, Siril Sam Abraham3, S Niranjan Kumar1
1 Department of Electronics and Communication Engineering, Karunya Institute of Technology and Sciences, Coimbatore, Tamil Nadu, India
2 Department of Computer Science Engineering, Karunya Institute of Technology and Sciences, Coimbatore, Tamil Nadu, India
3 Department of Data Science, Pivotchain Solution Technologies Private Limited, Pune, Maharashtra, India
Correspondence Address:
Dr. P Malin Bruntha
Department of Electronics and Communication Engineering, Karunya Institute of Technology and Sciences, Coimbatore - 641 114, Tamil Nadu
India
 Source of Support: None, Conflict of Interest: None  | Check |
DOI: 10.4103/jmp.jmp_61_21
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Purpose: In the field of medical diagnosis, deep learning-based computer-aided detection of diseases will reduce the burden of physicians in the diagnosis of diseases especially in the case of lung cancer nodule classification. Materials and Methods: A hybridized model which integrates deep features from Residual Neural Network using transfer learning and handcrafted features from the histogram of oriented gradients feature descriptor is proposed to classify the lung nodules as benign or malignant. The intrinsic convolutional neural network (CNN) features have been incorporated and they can resolve the drawbacks of handcrafted features that do not completely reflect the specific characteristics of a nodule. In the meantime, they also reduce the need for a large-scale annotated dataset for CNNs. For classifying malignant nodules and benign nodules, radial basis function support vector machine is used. The proposed hybridized model is evaluated on the LIDC-IDRI dataset. Results: It has achieved an accuracy of 97.53%, sensitivity of 98.62%, specificity of 96.88%, precision of 95.04%, F1 score of 0.9679, false-positive rate of 3.117%, and false-negative rate of 1.38% and has been compared with other state of the art techniques. Conclusions: The performance of the proposed hybridized feature-based classification technique is better than the deep features-based classification technique in lung nodule classification. |
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