Work

Brain tumor detection across diverse MR images: An automated triple-module approach integrating reduced fused deep features and machine learning

Year: 2024

Type: article

Abstract: Brain tumors pose a significant threat to human health due to their potential to disrupt normal brain function. Early and accurate detection is crucial for effective treatment. This study proposes a novel triple-module approach for automated brain tumor classification from MRI images. The first module utilizes pre-trained deep learning models (DenseNet121 and ResNet101) to extract informative features. The second module employs Principal Component Analysis (PCA) for dimensionality reduction. Finally, the third module utilizes a Random Forest classifier for tumor classification. The proposed model is evaluated on multiple datasets, demonstrating impressive performance (accuracy is >90 %) on both high-quality and noisy images. The results highlight the potential of this approach for improving brain tumor diagnosis and treatment planning.

Source: Results in Engineering

DOI: 10.1016/j.rineng.2024.103832

Authors: Yugal Pande, Jyotismita Chaki

Institution: Vellore Institute of Technology University

Language: English

Topic: Brain Tumor Detection and Classification

Subfield: Neurology

Field: Neuroscience

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $1,490