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2 Biocybernetics and Biomedical Engineering

2 2020

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Mediastinal lymph node malignancy detection in computed tomography images using fully convolutional network

Tekchandani Hitesh, Verma Shrish, Londhe Narendra D.

Biocybernetics and Biomedical Engineering

2020

Vol. 40, no. 1

187--199

Differential diagnosis of malignant and benign mediastinal lymph nodes (LNs) through invasive pathological tests is a complex and painful procedure because of sophisticated anatomical locations of LNs in the chest. The image based automatic machine learning techniques have been attempted in the past for malignancy detection. But these conventional methods suffer from complex selection of hand-crafted features and trade-off between performance parameters due to them. Today deep learning approaches are out-performing conventional machine learning techniques and able to overcome these issues. However, the existing convolutional neural network (CNN) based models also are prone to overfitting because of fully connected (FC) layers. Therefore, in this paper authors have proposed a fully convolutional network (FCN) based deep learning model for lymph nodes malignancy detection in computed tomography (CT) images. Moreover, the proposed FCN has been customized with batch normalization and advanced activation function Leaky ReLU to accelerate the training and to overcome the problem of dying ReLU, respectively. The performance of the proposed FCN has been also tuned to its best for smaller data size using data augmentation methods. The generalization of the proposed model is tested using the network parameter variation. To understand the reliability of the proposed model, it has also been compared with state-of-art related deep learning networks. The proposed FCN model has achieved an average accuracy, sensitivity, specificity, and area under curve as 90.28%, 90.63%, 89.95%, and 0.90, respectively. Our results also confirms the successful usabilility of augmentation methods for working on smaller datasets and deep learning approaches.

A deep Residual U-Net convolutional neural network for automated lung segmentation in computed tomography images

Khanna Anita, Londhe Narendra D., Gupta S., Semwal Ashish

Biocybernetics and Biomedical Engineering

2020

Vol. 40, no. 3

1314--1327

To improve the early diagnosis and treatment of lung diseases automated lung segmentation from CT images is a crucial task for clinical decision. The segmentation of the lung region from the CT scans is a very challenging task due to the irregular shape and size of lungs, low contrast and fuzzy boundaries of the lung. The manual segmentation of lung CT images is a laborious task. Therefore, various approaches are suggested by the researcher in the recent past for the automated lung segmentation. However, the existing approaches either utilize low-level handcraft features or CNN based Fully Convolutional Networks. The low-level hand-craft feature-based approaches lead to poor generalization, while the shallower networks are unable to extract more discriminative features. Hence, in this study, we have implemented a deep learning-based architecture called Residual U-Net with a false-positive removal algorithm for lung CT segmentation. Here, we have suggested that learning from a substantially deeper network with residual units can extract more discriminative feature representation as compared to shallow network for lung segmentation. To take full advantage of the deeper network, we have utilized a set of schemes to ensure efficient training. First, we have implemented a U-Net architecture with residual block to overcome the problem of performance degradation. Further, various data augmentation techniques are utilized to improve the generalization capability of the proposed method. The experimental results show that the proposed method achieved competitive results over the existing methods with DSC of 98.63%, 99.62% and 98.68% for LUNA16, VESSEL12 and HUG-ILD dataset respectively.