Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item 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.Item Reducing False Prediction on COVID-19 Detection Using Deep Learning(Institute of Electrical and Electronics Engineers Inc., 2021) Bhowmik, B.; Varna, S.A.; Kumar, A.; Kumar, R.This paper proposes a custom deep neural network-based scheme for coronavirus disease 2019 (COVID-19) detection. The proposed method takes X-ray images that use transfer learning techniques on pre-trained models. One objective of this work is to quickening the detection of the virus. Another goal is to reduce the number of falsely detected cases by a significant margin. The experimental setup demonstrates promising results on the selected dataset, which achieve up to 99.74%, 99.69%, 98.80% as classification, precision, and recall accuracy. © 2021 IEEE.Item Stress Detection Using Deep Learning Algorithms(Institute of Electrical and Electronics Engineers Inc., 2023) Angalakuditi, H.; Bhowmik, B.Stress has become a prevalent issue in modern society, with various negative impacts on mental and physical health. Stress in people is a physiological and psychological reaction to an imagined threat or difficulty. Several things, including employment, relationships, income, health problems, and significant life transitions, can cause stress. Depending on the person and the circumstance, stress symptoms can vary. They frequently include emotions of worry, irritation, and restlessness, as well as physical symptoms like headaches and muscle strain. Early stress detection is crucial for effective intervention and prevention of stress-related health issues. Detecting stress in real-time can be valuable in various domains such as healthcare, mental health, human-computer interaction, and workplace performance. This paper proposes a method for detecting stress using deep learning. A set of pre-trained models are employed for stress detection. The proposed technique is evaluated with publicly available datasets. Experimented results showed that the proposed stress detection method achieves accuracy in the range of 85.71-97.50% and the loss ranging from 0.4061 to 1.8144. © 2023 IEEE.