Nodule Detection with Convolutional Neural Network Using Apache Spark and GPU Frameworks
| Autor: | Dong-Ryeol Shin, Nikitha Johnsirani Venkatesan, Choon Sung Nam |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Computer science
Computation 02 engineering and technology lung nodule Convolutional neural network lcsh:Technology Field (computer science) 030218 nuclear medicine & medical imaging lcsh:Chemistry 03 medical and health sciences symbols.namesake 0302 clinical medicine Spark (mathematics) 0202 electrical engineering electronic engineering information engineering General Materials Science Sensitivity (control systems) Instrumentation lcsh:QH301-705.5 Fluid Flow and Transfer Processes Apache Spark business.industry lcsh:T Process Chemistry and Technology Deep learning Convolutional Neural Networks General Engineering deep learning Pattern recognition lcsh:QC1-999 Computer Science Applications ComputingMethodologies_PATTERNRECOGNITION lcsh:Biology (General) lcsh:QD1-999 Gaussian noise lcsh:TA1-2040 symbols 020201 artificial intelligence & image processing Noise (video) Artificial intelligence business lcsh:Engineering (General). Civil engineering (General) lcsh:Physics |
| Zdroj: | Applied Sciences, Vol 11, Iss 2838, p 2838 (2021) Applied Sciences Volume 11 Issue 6 |
| ISSN: | 2076-3417 |
| Popis: | In the pharmaceutical field, early detection of lung nodules is indispensable for increasing patient survival. We can enhance the quality of the medical images by intensifying the radiation dose. High radiation dose provokes cancer, which forces experts to use limited radiation. Using abrupt radiation generates noise in CT scans. We propose an optimal Convolutional Neural Network model in which Gaussian noise is removed for better classification and increased training accuracy. Experimental demonstration on the LUNA16 dataset of size 160 GB shows that our proposed method exhibit superior results. Classification accuracy, specificity, sensitivity, Precision, Recall, F1 measurement, and area under the ROC curve (AUC) of the model performance are taken as evaluation metrics. We conducted a performance comparison of our proposed model on numerous platforms, like Apache Spark, GPU, and CPU, to depreciate the training time without compromising the accuracy percentage. Our results show that Apache Spark, integrated with a deep learning framework, is suitable for parallel training computation with high accuracy. |
| Databáze: | OpenAIRE |
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