Our Projects

Intelligent Solutions for Health care Applications (ISHA)

Coronary Artery Plaque localization and characterization from 3D CCTA images (KLE Kore Hospital, Belagavi)

This research focuses on automating coronary artery segmentation and detecting the presence of plaques using 3D Coronary Computed Tomography Angiography (CCTA) images. The study aims to achieve precise identification of coronary arteries and analyze their structural characteristics. Accurate segmentation is essential for isolating the arteries from surrounding tissues, enhancing the clarity of critical regions. The goal is to develop a dependable tool that supports clinicians in early diagnosis and treatment, potentially improving patient outcomes and reducing the impact of cardiovascular diseases

Kangaroo Mother Care (KMC)
(JNMC KAHER, Belagavi)

Kangaroo Mother Care (KMC) (JNMC KAHER, Belagavi)

The "KANGA Suraksha" research project is a collaborative effort between KLE Academy of Higher Education & Research (KAHER), and focuses on the design and development of a continuous real-time monitoring device for Kangaroo Mother Care (KMC) compliance. This initiative aims to create a solution that ensures improving the health outcomes of premature and low birthweight infants. This collaboration emphasises the commitment to enhancing neonatal care and emphasises the importance of innovation in healthcare practices.

Metal Artifact Reduction (MAR) Project focuses on developing advanced imaging techniques to minimize distortions caused by metal objects in medical scans, such as CT or MRI. These artifacts, typically caused by implants, prosthetics, or dental work, can obscure critical diagnostic details and hinder accurate treatment planning. By ensuring precise artifact reduction, this initiative aims to enhance diagnostic accuracy and support better clinical outcomes.

X-ray computed tomography (CT) is an established diagnostic tool in clinical practice; however, there is growing concern regarding the increased risk of cancer induction associated with X-ray radiation exposure. The proposed research work demonstrates effective image post-processing using a frequency-band and sinogram-aware, self-guided network, which can effectively remove artifacts and recover missing detail from the contaminated sparse-view CT images.