Chest X-ray (CXR) is a low-cost medical imaging technique. It is a common procedure for the ident... more Chest X-ray (CXR) is a low-cost medical imaging technique. It is a common procedure for the identification of many respiratory diseases compared to MRI, CT, and PET scans. This paper presents the use of generative adversarial networks (GAN) to perform the task of lung segmentation on a given CXR. GANs are popular to generate realistic data by learning the mapping from one domain to another. In our work, the generator of the GAN is trained to generate a segmented mask of a given input CXR. The discriminator distinguishes between a ground truth and the generated mask, and updates the generator through the adversarial loss measure. The objective is to generate masks for the input CXR, which are as realistic as possible compared to the ground truth masks. The model is trained and evaluated using four different discriminators referred to as D1, D2, D3, and D4, respectively. Experimental results on three different CXR datasets reveal that the proposed model is able to achieve a dice-score of 0.9740, and IOU score of 0.943, which are better than other reported state-of-the art results. INDEX TERMS Deep learning, generative adversarial networks, lung segmentation, medical imaging.
Face image resolution and pose are two important factors that severely degrade the recognition ab... more Face image resolution and pose are two important factors that severely degrade the recognition ability. This paper presents a comparison of (i) the Wavelet Transform, (ii) the 2DPCA, (iii) the AdaBoost-LDA, and (iv) Fisherfaces based face recognition algorithms. Simulation results on the Multi-PIE database show that the 2DPCA face recognition algorithm can be reliably used for extremely low face image resolution of 15×15 pixels and from frontal (0°) to +35° of pose variation in near-real time. Whereas for high face image resolution of 40×40 pixels and up to 251×231 pixels, the Fisherfaces yields high accuracy across four different pose variation at the cost of much higher computation. Moreover, the recognition rate of the AdaBoost-LDA is unaffected by the image resolution from 251×231 down to 15×15 pixels. In addition, time cost comparison is also shown.
Chest X-ray (CXR) is a low-cost medical imaging technique. It is a common procedure for the ident... more Chest X-ray (CXR) is a low-cost medical imaging technique. It is a common procedure for the identification of many respiratory diseases compared to MRI, CT, and PET scans. This paper presents the use of generative adversarial networks (GAN) to perform the task of lung segmentation on a given CXR. GANs are popular to generate realistic data by learning the mapping from one domain to another. In our work, the generator of the GAN is trained to generate a segmented mask of a given input CXR. The discriminator distinguishes between a ground truth and the generated mask, and updates the generator through the adversarial loss measure. The objective is to generate masks for the input CXR, which are as realistic as possible compared to the ground truth masks. The model is trained and evaluated using four different discriminators referred to as D1, D2, D3 and D4 respectively. Experimental results on three different CXR datasets reveal that the proposed model is able to achieve a dice-score of 0.9740, and IOU score of 0.943, which are better than other reported state-of-the art results. INDEX TERMS Deep learning, generative adversarial networks, lung segmentation, medical imaging.
Chest X-ray (CXR) is a low-cost medical imaging technique. It is a common procedure for the ident... more Chest X-ray (CXR) is a low-cost medical imaging technique. It is a common procedure for the identification of many respiratory diseases compared to MRI, CT, and PET scans. This paper presents the use of generative adversarial networks (GAN) to perform the task of lung segmentation on a given CXR. GANs are popular to generate realistic data by learning the mapping from one domain to another. In our work, the generator of the GAN is trained to generate a segmented mask of a given input CXR. The discriminator distinguishes between a ground truth and the generated mask, and updates the generator through the adversarial loss measure. The objective is to generate masks for the input CXR, which are as realistic as possible compared to the ground truth masks. The model is trained and evaluated using four different discriminators referred to as D1, D2, D3, and D4, respectively. Experimental results on three different CXR datasets reveal that the proposed model is able to achieve a dice-score of 0.9740, and IOU score of 0.943, which are better than other reported state-of-the art results. INDEX TERMS Deep learning, generative adversarial networks, lung segmentation, medical imaging.
Face image resolution and pose are two important factors that severely degrade the recognition ab... more Face image resolution and pose are two important factors that severely degrade the recognition ability. This paper presents a comparison of (i) the Wavelet Transform, (ii) the 2DPCA, (iii) the AdaBoost-LDA, and (iv) Fisherfaces based face recognition algorithms. Simulation results on the Multi-PIE database show that the 2DPCA face recognition algorithm can be reliably used for extremely low face image resolution of 15×15 pixels and from frontal (0°) to +35° of pose variation in near-real time. Whereas for high face image resolution of 40×40 pixels and up to 251×231 pixels, the Fisherfaces yields high accuracy across four different pose variation at the cost of much higher computation. Moreover, the recognition rate of the AdaBoost-LDA is unaffected by the image resolution from 251×231 down to 15×15 pixels. In addition, time cost comparison is also shown.
Chest X-ray (CXR) is a low-cost medical imaging technique. It is a common procedure for the ident... more Chest X-ray (CXR) is a low-cost medical imaging technique. It is a common procedure for the identification of many respiratory diseases compared to MRI, CT, and PET scans. This paper presents the use of generative adversarial networks (GAN) to perform the task of lung segmentation on a given CXR. GANs are popular to generate realistic data by learning the mapping from one domain to another. In our work, the generator of the GAN is trained to generate a segmented mask of a given input CXR. The discriminator distinguishes between a ground truth and the generated mask, and updates the generator through the adversarial loss measure. The objective is to generate masks for the input CXR, which are as realistic as possible compared to the ground truth masks. The model is trained and evaluated using four different discriminators referred to as D1, D2, D3 and D4 respectively. Experimental results on three different CXR datasets reveal that the proposed model is able to achieve a dice-score of 0.9740, and IOU score of 0.943, which are better than other reported state-of-the art results. INDEX TERMS Deep learning, generative adversarial networks, lung segmentation, medical imaging.
Uploads
Papers by Faizan Munawar