Dimensionality Reduction in Deep Learning for Chest X-Ray Analysis of Lung Cancer

Efficiency of some dimensionality reduction techniques, like lung segmentation, bone shadow exclusion, and t-distributed stochastic neighbor embedding (t-SNE) for exclusion of outliers, is estimated for analysis of chest X-ray (CXR) 2D images by deep learning approach to help radiologists identify marks of lung cancer in CXR. Training and validation of the simple convolutional neural network (CNN) was performed on the open JSRT dataset (dataset #01), the JSRT after bone shadow exclusion - BSE-JSRT (dataset #02), JSRT after lung segmentation (dataset #03), BSE-JSRT after lung segmentation (dataset #04), and segmented BSE-JSRT after exclusion of outliers by t-SNE method (dataset #05). The results demonstrate that the pre-processed dataset obtained after lung segmentation, bone shadow exclusion, and filtering out the outliers by t-SNE (dataset #05) demonstrates the highest training rate and best accuracy in comparison to the other pre-processed datasets.Comment: 6 pages, 14 figure

Automatic algorithm for determining bone and soft-tissue factors in dual-energy subtraction chest radiography

Lung cancer is currently the first leading cause of worldwide cancer deaths since the early stage of lung cancer detection is still a challenge. In lung diagnosis, nodules sometimes overlap with ribs and tissues on lung chest radiographic images, which are complex for doctors and radiologists. Dual-energy subtraction (DES) is a suitable solution to solve those issues. This article will develop an efficient iterative DES for lung chest radiographic images. Moreover, we propose an automatic algorithm for accurately determining bone and soft-tissue factors for subtraction. The proposed algorithm for determining the bone and soft-tissue factors is based on window/level ratio and radiographic histogram analysis. First, we take the image sampling from the original size 3072 × 3072 to 512 × 512 to reduce the processing time while achieving the bone and soft-tissue factors. Next, we compute the window/level ratio on the soft-tissue image. Finally, we determine the minimum value of the ratio to obtain the optimal soft-tissue and bone factors. Our experimental results show that our proposed algorithm achieves a minimized runtime of 200 ms, outperforming the GE algorithm’s time of 4 s. The runtime of our DES of 6.066 s is shorter than the Fujifilm algorithm of 10 s while visualizing nodules on soft-tissue images and obtaining a similar quality of the soft-tissue images compared with the other algorithms. The academic contributions include the proposed algorithm for determining bone and soft-tissue factors and the optimized iterative DES algorithm to minimize time and dose consumption