Abstract
Lung cancer creates pulmonary nodules in the patient’s lung, which may be diagnosed early on using computer-aided diagnostics. A novel automated pulmonary nodule diagnosis technique using three-dimensional deep convolutional neural networks and multi-layered filter has been presented in this paper. For the suggested automated diagnosis of lung nodule, volumetric computed tomographic images are employed. The proposed approach generates three-dimensional feature layers, which retain the temporal links between adjacent slices of computed tomographic images. The use of several activation functions at different levels of the proposed network results in increased feature extraction and efficient classification. The suggested approach divides lung volumetric computed tomography pictures into malignant and benign categories. The suggested technique’s performance is evaluated using three commonly used datasets in the domain: LUNA 16, LIDC-IDRI, and TCIA. The proposed method outperforms the state-of-the-art in terms of accuracy, sensitivity, specificity, F-1 score, false-positive rate, false-negative rate, and error rate.
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Abbreviations
- CAD:
-
Computer-aided diagnosis
- KNN:
-
K-nearest neighbor
- MLF:
-
Multi-layered filter
- GP:
-
Genetic programming
- DCNN:
-
Deep convolutional neural network
- CT:
-
Computed tomography
- ANN:
-
Artificial neural network
- CNN:
-
Convolutional neural network
- FROC:
-
Free-response receiver operating characteristic
- AC:
-
Active contour
- CPM:
-
Computational precision medicine
- FL:
-
Fuzzy logic
- ROI:
-
Region of interest
- SVM:
-
Support vector machine
- GMF:
-
Geometric mean filter
- DBN:
-
Deep belief network
- R-CNN:
-
Recurrent convolutional neural network
- MSP:
-
Multi-segmented parallel
- RFCN:
-
Region-based fully convolutional layer
- ELM:
-
Extreme learning machine
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EAS gives this novel idea. EAS write all the manuscript. VC and MS draw some figures. EAS complete all the necessary methodology. VC gives the idea of writing the manuscript. EAS and VC review complete paper before submitting.
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Siddiqui, E.A., Chaurasia, V. & Shandilya, M. Classification of lung cancer computed tomography images using a 3-dimensional deep convolutional neural network with multi-layer filter. J Cancer Res Clin Oncol 149, 11279–11294 (2023). https://doi.org/10.1007/s00432-023-04992-9
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DOI: https://doi.org/10.1007/s00432-023-04992-9