Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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Author: search.filters.author.Sumam David, S.Subject: search.filters.subject.Deep Learning

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    Motion Deblurring of Faces
    (Institute of Electrical and Electronics Engineers Inc., 2020) Anand, P.; Sumam David, S.; Sudeep, K.S.
    This paper evaluates learning-based data-driven models for deblurring of facial images. Existing algorithms for deblurring, when used for facial images, often fail to preserve the facial shape and identity information. The best available models, which are used for general-purpose image deblurring, are pre-trained using only facial images. The Peak Signal to Noise Ratio (PSNR) Structural Similarity Index Measure (SSIM) and Time to deblur single images are the key metrics used for evaluating the models and for finding the most efficient model for deblurring facial images. From the results, the observation is that even though the PSNR value for DeblurGANv2 model is the highest, the best trade off between PSNR, SSIM, Time to deblur and visual quality is seen in DeblurGAN model. © 2020 IEEE.
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    Identification of Myeloproliferative Neoplasms using Deep Learning
    (Institute of Electrical and Electronics Engineers Inc., 2024) Abraham, S.; Penchalareddy, B.; Sumam David, S.; Vijayasenan, D.; Sridevi, H.B.
    Myeloproliferative Neoplasms (MPNs) are a heterogeneous group of disorders characterized by proliferation of one or more hematologic cell. These myeloproliferative disorders have different morphological features associated with it. Microscopic studies and morphological evaluation becomes mandatory in all these cases to reach a proper diagnosis. In this paper, we are trying to exploit the morphological features using deep learning techniques to narrow down the region of interest of MPNs. Here semantic segmentation is performed and the various types of MPNs (Benign, ET, MF, PV and CML) are classified. To perform this task, we have used MobileUNet and ResUNet++ deep learning network architectures and the performance is evaluated using F-scores and accuracy of the corresponding classes. The two models were compared and MobileUNet model is giving a better performance with an average F-score of 62% and ResUNet++ is having an average F-score of 59%. © 2024 IEEE.
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    Adversarial Learning Based Semi-supervised Semantic Segmentation of Low Resolution Gram Stained Microscopic Images
    (Springer Science and Business Media Deutschland GmbH, 2024) Singh, H.; Kanabur, V.R.; Sumam David, S.; Vijayasenan, D.; Govindan, S.
    Urinary tract infections (UTIs) are infections that affect the urinary system. It is usually caused by bacteria and pus cells. Analyzing urine samples, including examining pus cells, is a standard method for diagnosing and monitoring UTIs. However, manually detecting bacteria or pus cells in microscopic urine images is a time-consuming and labour-intensive task for microbiologists. Therefore, the segmentation of microscopic pus cell images will ease the process of detecting UTI. Especially low resolution microscopic images are hard to annotate; therefore, in this study, we propose an adversarial learning based semi-supervised segmentation method for segmentation of pus cell images at low resolution i.e. 40× using labeled high resolution images i.e. 100×. The proposed methodology aims to ease the process of UTI detection by automating the segmentation of pus cell images. The results of the proposed methodology demonstrate an increase in the Dice coefficient score percentage by 1%, 1.6% and 2.4% on 40× images when compared to fully supervised segmentation model trained on only 100× data using three different architectures- Unet, ResUnet++, and PSPnet, respectively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.