Kumar S Arun, Sasikala S
Department of Electronics and Communication Engineering Kumaraguru College of Technology India.
Curr Med Imaging. 2023 Mar 28. doi: 10.2174/1573405620666230328092218.
Brain tumour detection and classification require trained radiologists for efficient diagnosis. The proposed work aims to build a Computer Aided Diagnosis (CAD) tool to automate brain tumour detection using Machine Learning (ML) and Deep Learning (DL) techniques.
Magnetic Resonance Image (MRI) collected from the publicly available Kaggle dataset is used for brain tumour detection and classification. Deep features extracted from the global pooling layer of Pretrained Resnet18 network are classified using 3 different ML Classifiers, such as Support vector Machine (SVM), K-Nearest Neighbour (KNN), and Decision Tree (DT). The above classifiers are further hyperparameter optimised using Bayesian Algorithm (BA) to enhance the performance. Fusion of features extracted from shallow and deep layers of the pretrained Resnet18 network followed by BA-optimised ML classifiers is further used to enhance the detection and classification performance. The confusion matrix derived from the classifier model is used to evaluate the system's performance. Evaluation metrics, such as accuracy, sensitivity, specificity, precision, F1 score, Balance Classification Rate (BCR), Mathews Correlation Coefficient (MCC) and Kappa Coefficient (Kp), are calculated.
Maximum accuracy, sensitivity, specificity, precision, F1 score, BCR, MCC, and Kp of 99.11 %, 98.99 %, 99.22 %, 99.09 %, 99.09 %, 99.10 %, 98.21 %, 98.21 %, respectively, were obtained for detection using fusion of shallow and deep features of Resnet18 pretrained network classified by BA optimized SVM classifier. Feature fusion performs better for classification task with accuracy, sensitivity, specificity, precision, F1 score, BCR, MCC and Kp of 97.31 %, 97.30 %, 98.65 %, 97.37 %, 97.34 %, 97.97%, 95.99 %, 93.95 %, respectively.
The proposed brain tumour detection and classification framework using deep feature extraction from Resnet 18 pretrained network in conjunction with feature fusion and optimised ML classifiers can improve the system performance. Henceforth, the proposed work can be used as an assistive tool to aid the radiologist in automated brain tumour analysis and treatment.
脑肿瘤的检测和分类需要训练有素的放射科医生才能进行高效诊断。本研究旨在构建一种计算机辅助诊断（CAD）工具，利用机器学习（ML）和深度学习（DL）技术实现脑肿瘤检测自动化。
从公开可用的Kaggle数据集中收集的磁共振图像（MRI）用于脑肿瘤检测和分类。使用3种不同的ML分类器，如支持向量机（SVM）、K近邻（KNN）和决策树（DT），对从预训练的Resnet18网络全局池化层提取的深度特征进行分类。使用贝叶斯算法（BA）对上述分类器进行进一步的超参数优化，以提高性能。预训练的Resnet18网络浅层和深层提取的特征融合，再结合BA优化的ML分类器，进一步用于提高检测和分类性能。分类器模型得出的混淆矩阵用于评估系统性能。计算准确率、灵敏度、特异性、精确率、F1分数、平衡分类率（BCR）、马修斯相关系数（MCC）和卡帕系数（Kp）等评估指标。
使用由BA优化的SVM分类器分类的Resnet18预训练网络的浅层和深层特征融合进行检测时，分别获得了99.11%、98.99%、99.22%、99.09%、99.09%、99.10%、98.21%、98.21%的最高准确率、灵敏度、特异性、精确率、F1分数、BCR、MCC和Kp。特征融合在分类任务中表现更好，准确率、灵敏度、特异性、精确率、F1分数、BCR、MCC和Kp分别为97.31%、97.30%、98.65%、97.37%、97.34%、97.97%、95.99%、93.95%。
所提出的利用从Resnet 18预训练网络中提取深度特征并结合特征融合和优化的ML分类器的脑肿瘤检测和分类框架可以提高系统性能。今后，所提出的工作可以用作辅助工具，帮助放射科医生进行脑肿瘤的自动化分析和治疗。
