A Sugeno-fuzzy integral fusion framework for brain tumor identification using transfer learning models

Identification of a brain tumour is a very crucial task in medical diagnostics. It requires timely and accurate detection for effective treatment that improves the outcomes of patients. This challenge involves handling the variability and complexity of tumour imagery with high precision. The present work addresses these challenges by presenting a new framework that integrates TL with the Sugeno-Fuzzy Integral (SFI) for enhanced BT classification. Three different advanced TL models, namely ResNet-164, SqueezeNet, and DenseNet-201, will be used, combined with the SFI to aggregate their predictions for improving the accuracy of BT classification. For experimentation, two datasets are used: Dataset 1 consists of three tumour classes, namely glioma, pituitary, and meningioma, while Dataset 2 consists of four classes, adding the normal class. Preparation of the dataset included resizing, filtering, and balancing the dataset, followed by training and testing with a split of 70:30. Then, the developed model was tested on two MRI benchmark datasets, where classification accuracy of 99.19% was achieved for a three-class dataset and 98.92% for a four-class dataset, outperforming both individual and traditional ensemble models.