  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.
