SV
SHEETAL VIJAY KULKARNI
AISSMS Institute of Information Technology
Research areas: Computer Science · Computer Vision and Pattern Recognition · Medical Image Segmentation Techniques · Brain Tumor Detection and Classification · Image Retrieval and Classification Techniques
About
Medical Image Processing, Medical Internet of Things
Employment
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AISSMS Institute of Information Technology Assistant Professor2009 - Present
Education
Education history is unavailable.
Projects & Funding
Projects & funding information is unavailable.
Publications (16)
- Automatic Car Washing System Using Automation Studio Save
- Edge-Assisted IoT Framework for Real-Time Medical Image Analysis Using Smart Sensors and Federated Deep Learning Save
- HRNet: Hybrid Residual Network and Spatial Attention-based Bilateral Network with Weighted Feature Selection for Brain Tumor Segmentation and Classification Save
- Autonomous Robotics for Precision Manufacturing and Healthcare Automation Using AI, Computer Vision, and Nanotechnology-Enhanced Components Save
- AI and IIoT Enabling Smart Connectivity and Automation in Healthcare Save
- WIRELESS LEVEL MEASUREMENT USING PLC Save
- DATA ANALYTICS IN BIOMEDICAL INSTRUMENTATION: TRANSFORMING PATIENT CARE Save
- AI and IIoT Enabling Smart Connectivity and Automation in Healthcare Save
- Autonomous Robotics for Precision Manufacturing and Healthcare Automation Using AI, Computer Vision, and Nanotechnology-Enhanced Components Save
- Explainable Federated AI Framework for Privacy-Aware Medical Image Processing and Diagnostic Decision Support Systems Save
- Hybrid Deep Learning Techniques for Enhanced Detection of Tumors and Abnormalities in Medical Imaging Data Save
- Multi-scale based Network and Adaptive EfficientnetB7 with ASPP: Analysis of Novel Brain Tumor Segmentation and Classification Save
- Personalized Patient Risk Prediction Using Multi-modal AI on EHR and Medical Imaging Data Save
- Hybrid Powered Router Save
- Analysis of Brain Disorder Neuroimages by Feature Extraction Save
- Image Registration And Recognition Of Brain Images Captured Using Multiple Modalities For Early Detection Save