LHW-Net: An ensemble-based machine learning framework for brain tumor classification

Clinical Snapshot

Key Findings

• P Value: Not reported

• Effect Size: Not reported

• Primary Outcome: Brain tumor classification accuracy (specific values not reported in abstract)

• Nnt Or Sensitivity: Sensitivity and specificity values not reported

• Confidence Interval: Not reported

Clinical Application

Requires computational infrastructure and integration with existing radiology workflows Would need TGA approval as medical device software, integration with existing radiology information systems, and validation against Australian population demographics Patients requiring brain tumor classification from medical imaging, pending validation in clinical cohorts

Abstract

The classification of brain tumors is an unsolved problem associated with heterogeneity of tumors and fluctuations in imaging conditions. In this work, the investigation introduces a powerful novel framework, named LHW-Net that combines handcrafted features called local binary patterns (LBP), histogram of oriented gradients (HOG), and wavelet transform (WT). Within the LHW-Net framework, the extracted features are utilized in different machine learning classifiers, such as K-Nearest Neighbors (KNN), Random Forest (RF) and Support Vector Classifier (SVC). The results of the individual classifiers are further combined using probabilistic score fusion approach to improve classification performance. The effectiveness and robustness of the proposed work are validated by the achieved experimental results on commonly accepted benchmark datasets.