Work

Medical Image, Analysis and Visualization using Image Processing

Year: 2024

Type: article

Abstract: This project presents an automated approach for detecting brain tumor using Magnetic Resonance Imaging (MRI) and image processing techniques. Traditional methods rely on human inspection, which can be error-prone and time consuming. Our framework consists of several modules: acquiring MRI images, preprocessing to enhance image quality (including noise reduction and edge detection), and segmenting the tumor using the Watershed algorithm. We extract texture features from the segmented images using the Gray Level Co-occurrence Matrix (GLCM), focusing on metrics like energy, contrast, correlation, and homogeneity. Finally, we classify the MRI images as normal or abnormal using a Multilayer Perceptron (MLP) model. This automated detection process improves the efficiency of tumor identification, providing insights into the size, shape, and position of the tumor while alleviating the workload of radiologists.

Source: International Journal of Advanced Research in Science Communication and Technology

DOI: 10.48175/ijarsct-22153

Authors: Sakshi Dawale, Vaishnavi Salunke, Roshan Shewale, Vikas Tekawade, Dr. Deepali Sale

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: W4404246655

FWCI:

Cites: 4

Cited by:

Related to: 10