Achieving high accuracy with computational efficiency in brain disease classification from Magnetic Resonance Imaging (MRI) scans is challenging, particularly when both coarse and fine-grained distinctions are crucial. Current deep learning methods often struggle to balance accuracy with computational demands. We propose Lite-FBCN, a novel Lightweight Fast Bilinear Convolutional Network designed to address this issue. Unlike traditional dual-network bilinear models, Lite-FBCN utilizes a single-network architecture, significantly reducing computational load. Lite-FBCN leverages lightweight, pre-trained CNNs fine-tuned to extract relevant features and incorporates a channel reducer layer before bilinear pooling, minimizing feature map dimensionality and resulting in a compact bilinear vector. Extensive evaluations on cross-validation and hold-out data demonstrate that Lite-FBCN not only surpasses baseline CNNs but also outperforms existing bilinear models. Lite-FBCN with MobileNetV1 attains 98.10% accuracy in cross-validation and 69.37% on hold-out data (a 3% improvement over the baseline). UMAP visualizations further confirm its effectiveness in distinguishing closely related brain disease classes. Moreover, its optimal trade-off between performance and computational efficiency positions Lite-FBCN as a promising solution for enhancing diagnostic capabilities in resource-constrained and or real-time clinical environments.

翻译：在磁共振成像（MRI）扫描的脑疾病分类任务中，同时实现高精度与计算效率是一项挑战，尤其是在粗粒度和细粒度区分都至关重要的情况下。当前的深度学习方法往往难以在精度与计算需求之间取得平衡。我们提出了Lite-FBCN，一种新颖的轻量级快速双线性卷积网络，旨在解决这一问题。与传统的双网络双线性模型不同，Lite-FBCN采用单网络架构，显著降低了计算负载。Lite-FBCN利用经过微调的轻量级预训练CNN来提取相关特征，并在双线性池化之前引入通道缩减层，以最小化特征图的维度，从而生成紧凑的双线性向量。在交叉验证和留出数据上进行的大量评估表明，Lite-FBCN不仅超越了基线CNN，也优于现有的双线性模型。采用MobileNetV1的Lite-FBCN在交叉验证中达到了98.10%的准确率，在留出数据上达到69.37%（较基线提升3%）。UMAP可视化进一步证实了其在区分密切相关的脑疾病类别方面的有效性。此外，其在性能与计算效率之间的最佳权衡，使Lite-FBCN成为一个有前景的解决方案，可提升资源受限和/或实时临床环境中的诊断能力。