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International Journal of Medical Research & Health Sciences (IJMRHS)
ISSN: 2319-5886 Indexed in: ESCI (Thomson Reuters)

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Abstract

Nodular-Deep: Classification of Pulmonary Nodules using Deep Neural Network

Author(s): Qaisar Abbas

Pulmonary nodules represent higher malignancy rate and an accurate detection is very crucial when clinically diagnosis by radiologists from high-resolution computed tomography (HRCT) images. At an early stage, if lung cancer is not diagnosis then it leads toward death. In the past studies, it noticed that many computer-aided diagnostic (CADe) system for classification of lung nodules are developed but tested on the limited dataset and focused on domain expert knowledge. Therefore, those CADx systems were not suitable for large-scale environments. To address these issues, an efficient and effective CADe system is developed to classify the pulmonary lung nodules into benign and malignant classes. In this paper, a new CADe system is implemented through the integration of variants of advanced deep learning algorithms known as Nodular-Deep. Convolutional neural network (CNN) and recurrent neural network (RNN) algorithms are combined with softmax linear classifier without using hand-crafted features and any pre- or post-processing steps. The Nodular-Deep system is tested on the 1200 scans obtained from LIDC-IDRI database covers a set of 2600 pulmonary nodules. This dataset contains an equal number of benign (non-cancerous) and malignant (cancerous) nodules. The performance of nodular-deep system is evaluated through 10-fold cross validation test through the statistical metrics such as sensitivity (SE), Specificity (SP) and area under the receiver operating curve (AUC). On this 2600 pulmonary nodules, the Nodular-Deep system is achieved on average result such as 94% of SE, 96% of SP and 0.95 of AUC. This obtained results demonstrate that this nodular-Deep system outperforms compared to manual segmentation by a radiologist.


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    International Journal of Medical Research & Health Sciences © 2012-2019 [Last updated: March 19, 2019]