Papers by AV Krishnarao PADYALA
Journal of Information Systems Engineering and Management, 2025
Medical imaging remains a cornerstone of modern healthcare, essential for accurate disease detect... more Medical imaging remains a cornerstone of modern healthcare, essential for accurate disease detection and optimized treatment planning. This review examines advanced imaging technologies such as X-ray, CT, MRI, and ultrasound, alongside emerging methodologies incorporating machine learning (ML) and artificial intelligence (AI). Techniques for disease detection focus on identifying abnormalities, lesions, or pathological transformations, while strategies for volumetric reduction address minimizing affected tissues or organs. The integration of these approaches facilitates timely interventions and aids in evaluating treatment efficacy with precision. Despite significant advancements, challenges persist, including enhancing detection sensitivity, improving volumetric accuracy, and effectively integrating multi-modal imaging datasets. This discussion emphasizes current innovations, barriers to progress, and future directions, advocating for solutions that advance personalized healthcare. Furthermore, the role of mobile applications for efficient processing and analysis, combined with the scalability of cloud storage solutions, underscores the importance of leveraging technology to address contemporary medical imaging demands.
Mobile volume rendering and disease detection using deep learning algorithms
Journal of autonomous intelligence, May 23, 2024
This paper introduces a system designed to convert 2D slices from Magnetic resonance imaging(MRI)... more This paper introduces a system designed to convert 2D slices from Magnetic resonance imaging(MRI) and Computed Tomography(CT) scans into 3D images, facilitating mobile device-based diagnosis by medical professionals. Utilizing machine learning techniques tailored to specific image categories, the system processes Digital Imaging and Communications in Medicine(DICOM) images for disease detection. AWS cloud infrastructure, including S3 bucket, Relational Database Service(RDS), and DynamoDB, manages DICOM storage. The system delivers a final processed image displaying predicted diseases directly to the mobile screen. This innovative approach enhances medical imaging accessibility and diagnostic accuracy, offering a streamlined solution for healthcare professionals.
Mathematical Statistician and Engineering Applications, 2023
To diagnose a variety of conditions, from torn ligaments to tumors,
doctors make use of MRI and ... more To diagnose a variety of conditions, from torn ligaments to tumors,
doctors make use of MRI and CT scans. An application developed using
python, installed in a mobile or tablet render the data of MRI and CT
scans and let the doctor understand patient health condition. A method for
transforming MRI/CT scan data in DICOM form to HDF5 with python.
Variety of tools available now to develop android applications for mobile
with python.
Journal of Autonomous Intelligence, 2024
This paper introduces a system designed to convert 2D slices from Magnetic resonance imaging(MRI)... more This paper introduces a system designed to convert 2D slices from Magnetic resonance imaging(MRI) and
Computed Tomography(CT) scans into 3D images, facilitating mobile device-based diagnosis by medical professionals.
Utilizing machine learning techniques tailored to specific image categories, the system processes Digital Imaging and
Communications in Medicine(DICOM) images for disease detection. AWS cloud infrastructure, including S3 bucket,
Relational Database Service(RDS), and DynamoDB, manages DICOM storage. The system delivers a final processed
image displaying predicted diseases directly to the mobile screen. This innovative approach enhances medical imaging
accessibility and diagnostic accuracy, offering a streamlined solution for healthcare professionals.
Medical imaging has become one of the most used diagnostic tools in the medical profession in the... more Medical imaging has become one of the most used diagnostic tools in the medical profession in the last three decades. Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) technologies have become widely adopted because of their ability to capture the human body in a non-invasive manner. A volumetric dataset is a series of orthogonal 2D slices captured at a regular interval, typically along the axis of the body from the head to the feet. Volume rendering is a computer graphics technique that allows volumetric data to be visualized and manipulated as a single 3D object. Some of the volume rendering methods are Isosurface rendering, image splatting , shear warp, texture slicing, and raycasting. CT and MRI hardware was limited to providing a single 3D scan of the human body. Functional imaging let capture of anatomical data over time.One of them is Functional MRI (fMRI), is used to capture changes in the human body over time.This paper presents creation of generic software capa...
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Papers by AV Krishnarao PADYALA
doctors make use of MRI and CT scans. An application developed using
python, installed in a mobile or tablet render the data of MRI and CT
scans and let the doctor understand patient health condition. A method for
transforming MRI/CT scan data in DICOM form to HDF5 with python.
Variety of tools available now to develop android applications for mobile
with python.
Computed Tomography(CT) scans into 3D images, facilitating mobile device-based diagnosis by medical professionals.
Utilizing machine learning techniques tailored to specific image categories, the system processes Digital Imaging and
Communications in Medicine(DICOM) images for disease detection. AWS cloud infrastructure, including S3 bucket,
Relational Database Service(RDS), and DynamoDB, manages DICOM storage. The system delivers a final processed
image displaying predicted diseases directly to the mobile screen. This innovative approach enhances medical imaging
accessibility and diagnostic accuracy, offering a streamlined solution for healthcare professionals.