Automated Bi-Fold Weighted Ensemble Algorithms and its Application to Brain Tumor Detection and Classification

TL;DR

This paper introduces two innovative bi-fold weighted ensemble algorithms that enhance brain tumor detection accuracy by combining multiple classifiers and optimizing weight assignment, demonstrating superior performance over traditional methods.

Contribution

The study presents novel bi-fold weighted ensemble models with an unsupervised weight calculation schema, improving classification performance in brain tumor detection.

Findings

Enhanced accuracy in brain tumor classification

Superior performance compared to standard soft voting techniques

Effective integration of CNN, VGG-16, and InceptionResNetV2 models

Abstract

The uncontrolled and unstructured growth of brain cells is known as brain tumor, which has one of the highest mortality rates among diseases from all types of cancers. Due to limited diagnostic and treatment capabilities, they pose significant challenges, especially in third-world countries. Early diagnosis plays a vital role in effectively managing brain tumors and reducing mortality rates. However, the availability of diagnostic methods is hindered by various limitations, including high costs and lengthy result acquisition times, impeding early detection of the disease. In this study, we present two cutting-edge bi-fold weighted voting ensemble models that aim to boost the effectiveness of weighted ensemble methods. These two proposed methods combine the classification outcomes from multiple classifiers and determine the optimal result by selecting the one with the highest probability in the first approach, and the highest weighted prediction in the second technique. These approaches significantly improve the overall performance of weighted ensemble techniques. In the first proposed method, we improve the soft voting technique (SVT) by introducing a novel unsupervised weight calculating schema (UWCS) to enhance its weight assigning capability, known as the extended soft voting technique (ESVT). Secondly, we propose a novel weighted method (NWM) by using the proposed UWCS. Both of our approaches incorporate three distinct models: a custom-built CNN, VGG-16, and InceptionResNetV2 which has been trained on publicly available datasets. The effectiveness of our proposed systems is evaluated through blind testing, where exceptional results are achieved. We then establish a comparative analysis of the performance of our proposed methods with that of SVT to show their superiority and effectiveness.