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Found 9 papers, 0 papers with code
DeepMediX: A Deep Learning-Driven Resource-Efficient Medical Diagnosis Across the Spectrum
In the rapidly evolving landscape of medical imaging diagnostics, achieving high accuracy while preserving computational efficiency remains a formidable challenge.
Vision Through the Veil: Differential Privacy in Federated Learning for Medical Image Classification
The proliferation of deep learning applications in healthcare calls for data aggregation across various institutions, a practice often associated with significant privacy concerns.
See Through the Fog: Curriculum Learning with Progressive Occlusion in Medical Imaging
In this paper, we propose a novel curriculum learning-based approach to train deep learning models to handle occluded medical images effectively.
ErfReLU: Adaptive Activation Function for Deep Neural Network
In this paper, a novel activation function 'ErfReLU' has been developed based on the erf function and ReLU.
Transcending Grids: Point Clouds and Surface Representations Powering Neurological Processing
In healthcare, accurately classifying medical images is vital, but conventional methods often hinge on medical data with a consistent grid structure, which may restrict their overall performance.
Time Domain Simulation of DFIG-Based Wind Power System using Differential Transform Method
This paper proposes a new non-iterative time-domain simulation approach using Differential Transform Method (DTM) to solve the set of non-linear Differential-Algebraic Equations (DAEs) involved in a DFIG-based wind power system.
Dense Representative Tooth Landmark/axis Detection Network on 3D Model
Results show that our method can produce tooth landmarks with high accuracy.
Software Effort Estimation using parameter tuned Models
Software estimation is one of the most important activities in the software project.
Multi-Objective Evolutionary approach for the Performance Improvement of Learners using Ensembling Feature selection and Discretization Technique on Medical data
Biomedical data is filled with continuous real values; these values in the feature set tend to create problems like underfitting, the curse of dimensionality and increase in misclassification rate because of higher variance.
