Work

U-DENSENET Deep Learning Model for Medical Image Segmentation

Year: 2024

Type: article

Abstract: Medical image segmentation, particularly in brain MRIs, is critical for identifying neurological disorders and planning treatment. This paper presents a novel deep learning U-DenseNet model that combines the strengths of DenseNet and UNet architectures for improved brain tumor segmentation and skull-stripped segmentation. Evaluated on both skull-stripped and brain tumor datasets, the U-DenseNet model achieves a Dice coefficient of 0.9125 and accuracy of 99.81\% for brain tumor segmentation and a Dice coefficient of 0.9902 and accuracy of 98.49\% for skull-stripped segmentation. The architecture of U-DenseNet model effectively captures fine anatomical details while ensuring computational efficiency, making it suitable for clinical applications.

Source: VAWKUM Transactions on Computer Sciences

DOI: 10.21015/vtcs.v12i2.1922

Authors: Ali Gul Sarparrah, Noor Ahmed, Muhammad Dawood, Shumaila Husain, Sher Mohammad, Mumtaz Ahmed Dehwar

Institutions: Sardar Bahadur Khan Women's University, Balochistan University of Engineering and Technology

Language: English

Topic: Brain Tumor Detection and Classification

Subfield: Neurology

Field: Neuroscience

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: diamond

OpenAlex ID: W4406833964

FWCI:

Cites: 18

Cited by:

Related to: 10