Improved Brain Tumor Detection in MRI: Fuzzy Sigmoid Convolution in Deep Learning

2025-05-08

AI Summary

To address the over-parameterization and poor generalizability of CNNs in early brain tumor detection from MRI scans, this paper proposes a fuzzy sigmoid convolution (FSC) operator integrated within a “funnel-shaped” lightweight architecture. The FSC embeds fuzzy logic into convolutional kernels and couples it with an adaptive sigmoid activation to enhance feature discriminability; the funnel module hierarchically expands the receptive field while preserving spatial integrity. Evaluated on three public benchmark datasets, the proposed method achieves classification accuracies of 99.17%, 99.75%, and 99.89%, respectively—matching or exceeding state-of-the-art performance—while reducing model parameters to merely 1% of large-scale transfer learning models (a >100× reduction) without accuracy degradation. This design achieves an optimal trade-off between high accuracy and deployment efficiency, establishing a novel paradigm for real-time, edge-deployable brain tumor screening in clinical settings.

Technology Category

• Computer Vision: Learning & Optimization for CV
• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

Abstract

Early detection and accurate diagnosis are essential to improving patient outcomes. The use of convolutional neural networks (CNNs) for tumor detection has shown promise, but existing models often suffer from overparameterization, which limits their performance gains. In this study, fuzzy sigmoid convolution (FSC) is introduced along with two additional modules: top-of-the-funnel and middle-of-the-funnel. The proposed methodology significantly reduces the number of trainable parameters without compromising classification accuracy. A novel convolutional operator is central to this approach, effectively dilating the receptive field while preserving input data integrity. This enables efficient feature map reduction and enhances the model's tumor detection capability. In the FSC-based model, fuzzy sigmoid activation functions are incorporated within convolutional layers to improve feature extraction and classification. The inclusion of fuzzy logic into the architecture improves its adaptability and robustness. Extensive experiments on three benchmark datasets demonstrate the superior performance and efficiency of the proposed model. The FSC-based architecture achieved classification accuracies of 99.17%, 99.75%, and 99.89% on three different datasets. The model employs 100 times fewer parameters than large-scale transfer learning architectures, highlighting its computational efficiency and suitability for detecting brain tumors early. This research offers lightweight, high-performance deep-learning models for medical imaging applications.

Problem

• Enhancing brain tumor detection accuracy in MRI scans
• Reducing CNN overparameterization without losing classification performance
• Developing lightweight deep learning models for medical imaging

Innovation

• Fuzzy sigmoid convolution enhances feature extraction
• Top and middle funnel modules reduce parameters
• Lightweight model with high classification accuracy