Enhanced Brain Tumor Segmentation: A Study on the Efficacy of Multi-Model Ensembling Utilizing Diverse Backbones

Gliomas account for approximately 80% of all malignant brain tumors and are associated with the most unfavourable survival rates amongst all brain tumor types. Segmenting these tumors is integral to evaluating their dimensions and morphology, formulating tailored treatment plans, and estimating patients’ life expectancy. Various approaches for brain tumor segmentation have been proposed, yet the model described herein stands out, offering promising results in this vital task. A comprehensive examination of three eminent convolutional neural network (CNN) models, namely Resnext50, Resnet50, and InceptionV3, was conducted, each characterised by its own unique structural framework. The enhancement of the segmentation approach’s effectiveness was a focal point, leading to the exploration of ensemble modeling. This approach leverages the collective strengths of the aforementioned models, facilitated by softmax averaging. This collaborative initiative culminated in the creation of the Ensemble model, which outshoned its individual counterparts in performance metrics. Furthermore, the Ensemble model is poised to instigate significant advancements in brain tumor diagnostic procedures. The ensemble methodology is instrumental in refining the precision and automation of brain tumor diagnoses. Deep learning, as evidenced by the research, is a transformative element in medical image analysis, elevating the accuracy and efficiency of brain tumor segmentation. Consequently, this refined approach is set to become an invaluable asset for medical professionals in enhancing the accuracy of diagnoses and the efficacy of treatments for patients afflicted with brain tumors.