Conference Papers

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

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Author: search.filters.author.Kamath S․, S.Subject: search.filters.subject.Deep Convolutional Neural Networks

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    Automatic Quality Enhancement of Medical Diagnostic Scans with Deep Neural Image Super-Resolution Models
    (Institute of Electrical and Electronics Engineers Inc., 2020) Karthik, K.; Kamath S․, S.; Kamath, S.U.
    In modern healthcare, diagnostic imaging is an essential component for diagnosing ailments and delivering quality healthcare. Given the variety in medical scanning techniques, a recurring issue across different modalities is that the scan quality is often affected by artifacts introduced by hardware and software faults in the imaging equipment. Significant challenges in the 3D Imaging Techniques include low quality/low-resolution scan images or the addition of unwanted artifacts due to patient movement. Researchers have put forth solutions ranging from machine learning algorithms like Gradient Descent to more complex Deep CNN models for rectifying these faults. In this paper, we aim to benchmark deep learning models for improving the quality of diagnostic images, through Super-resolution, for enabling faster and easier detection of anomalies that may be missed otherwise. Super-resolution CNN and Deep CNN architectures were employed for up-sampling medical scans for enhancing their quality. The CNN models were trained to learn motion artifact characteristics that are a result of patient movement and negate its effects in the super-resolved output. We present comparative results of six super-resolution models on a standard dataset and metrics. During the experimental evaluation, it was observed that the ResNet SRCNN model outperformed all other models used for comparison by a large margin, with an improvement of 4.87 to 8.68% over the other state-of-the-art models. © 2020 IEEE.
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    Deep Learning based detection of Diabetic Retinopathy from Inexpensive fundus imaging techniques
    (Institute of Electrical and Electronics Engineers Inc., 2021) Mukesh, B.R.; Harish, T.; Mayya, V.; Kamath S․, S.
    Diabetic Retinopathy is the leading cause of blindness across the world as per statistics published by the World Health Organization. Recently, there has been significant research on adopting deep learning methodologies to automate and improve the process of evaluating the advent and progress of chronic eye diseases using eye fundus images. Typically, eye fundus imaging equipment is used by trained specialists for evaluating eye health, however, fundus imaging tends to be expensive, and also the high-end equipment used is typically available in large hospitals and urban areas. This cost barrier leads to an imbalance in care between the developed and developing parts of the world. In this paper, we propose an inexpensive stand-in for such a device and a deep neural model pipeline that is able to analyze these images to determine the need for further evaluation from a trained ophthalmologist. The pipeline is able to achieve an AUC score of 0.9781 in detecting Referable DR. We also benchmark the proposed deep learning pipeline against other pipelines on standard datasets to demonstrate the capability of the network. © 2021 IEEE.