A novel approach for the detection of brain tumor and its classification via independent component analysis.

A brain tumor is regarded as one of the deadliest types of cancer due to its intricate nature.This is why it is important that patients get the best possible diagnosis and treatment options. With the help of machine vision, neurologists can now perform a more accurate and faster diagnosis.

Segmentation and severity classification of scar tissues in LGE-CMR images using HDResC-Net with Flamingo gannet search optimization.

Late gadolinium enhanced-cardiac magnetic resonance (LGE-CMR) images play a critical role in evaluating cardiac pathology, where scar tissue serves as a vital indicator impacting prognosis and treatment decisions. However, accurately segmenting scar tissues and assessing their severity present challenges due to complex tissue composition and imaging artifacts. Existing methods often lack precision and robustness, limiting their clinical applicability.

Assessment of Microstructure and Mechanical Properties of Stir Zone Seam of Friction Stir Welded Magnesium AZ31B through Nano-SiC.

In the present study, silicon carbide nanoparticles were incorporated into AZ31B magnesium alloy welded joints using the friction stir welding technique at five different stir zone volume fractions. The volume percentage of nano-SiC was varied from 0⁻20% in increments of 4%. Initially, the microstructure analyses of the V4, V8, and V12 welded joints were observed to be in good accordance with a homogeneous dispersion of nano SiC particles within the stir zone (SZ).