Mehmood Yasar, Bajwa Usama Ijaz
Department of Computer Science, COMSATS University Islamabad, Lahore Campus, Lahore, Punjab, Pakistan.
Digit Health. 2024 Sep 28;10:20552076241284920. doi: 10.1177/20552076241284920. eCollection 2024 Jan-Dec.
Brain tumor grade is an important aspect of brain tumor diagnosis and helps to plan for treatment. Traditional methods of diagnosis, including biopsy and manual examination of medical images, are either invasive or may result in inaccurate diagnoses. This study proposes a brain tumor grade classification technique using a modern convolutional neural network (CNN) architecture called ConvNext that inputs magnetic resonance imaging (MRI) data.
Deep learning-based techniques are replacing invasive procedures for consistent, accurate, and non-invasive diagnosis of brain tumors. A well-known challenge of using deep learning architectures in medical imaging is data scarcity. Modern-day architectures have huge trainable parameters and require massive datasets to achieve the desired accuracy and avoid overfitting. Therefore, transfer learning is popular among researchers using medical imaging data. Recently, transformer-based architectures have surpassed CNNs for image data. However, recently proposed CNNs have achieved superior accuracy by introducing some tweaks inspired by vision transformers. This study proposed a technique to extract features from the ConvNext architecture and feed these features to a fully connected neural network for final classification.
The proposed study achieved state-of-the-art performance on the BraTS 2019 dataset using pre-trained ConvNext. The best accuracy of 99.5% was achieved when three MRI sequences were input as three channels of the pre-trained CNN.
The study demonstrated the efficacy of the representations learned by a modern CNN architecture, which has a higher inductive bias for the image data than vision transformers for brain tumor grade classification.
脑肿瘤分级是脑肿瘤诊断的一个重要方面，有助于制定治疗方案。传统的诊断方法，包括活检和医学图像的人工检查，要么具有侵入性，要么可能导致诊断不准确。本研究提出了一种使用名为ConvNext的现代卷积神经网络（CNN）架构的脑肿瘤分级分类技术，该架构输入磁共振成像（MRI）数据。
基于深度学习的技术正在取代侵入性程序，以实现对脑肿瘤的一致、准确和非侵入性诊断。在医学成像中使用深度学习架构的一个众所周知的挑战是数据稀缺。现代架构有大量可训练参数，需要大量数据集才能达到所需的准确性并避免过拟合。因此，迁移学习在使用医学成像数据的研究人员中很受欢迎。最近，基于Transformer的架构在图像数据方面超过了CNN。然而，最近提出的CNN通过引入一些受视觉Transformer启发的调整，取得了更高的准确性。本研究提出了一种从ConvNext架构中提取特征的技术，并将这些特征输入到全连接神经网络中进行最终分类。
使用预训练的ConvNext，所提出的研究在BraTS 2019数据集上取得了领先的性能。当将三个MRI序列作为预训练CNN的三个通道输入时，实现了99.5%的最佳准确率。
该研究证明了现代CNN架构学习到的表征的有效性，对于脑肿瘤分级分类，该架构对图像数据的归纳偏差比视觉Transformer更高。
