Özbay Erdal, Altunbey Özbay Feyza
Firat University, Faculty of Engineering, Computer Engineering, 23119, Elazig, Turkey.
Firat University, Faculty of Engineering, Software Engineering, 23119, Elazig, Turkey.
Comput Methods Programs Biomed. 2023 Apr;231:107387. doi: 10.1016/j.cmpb.2023.107387. Epub 2023 Jan 31.
Brain tumor is a deadly disease that can affect people of all ages. Radiologists play a critical role in the early diagnosis and treatment of the 14,000 persons diagnosed with brain tumors on average each year. The best method for tumor detection with computer-aided diagnosis systems (CADs) is Magnetic Resonance Imaging (MRI). However, manual evaluation using conventional approaches may result in a number of inaccuracies due to the complicated tissue properties of a large number of images. Therefore a precision medical image hashing approach is proposed that combines interpretability and feature fusion using MRI images of brain tumors, to address the issue of medical image retrieval.
A precision hashing method combining interpretability and feature fusion is proposed to recover the problem of low image resolutions in brain tumor detection on the Brain-Tumor-MRI (BT-MRI) dataset. First, the dataset is pre-trained with the DenseNet201 network using the Comparison-to-Learn method. Then, a global network is created that generates the salience map to yield a mask crop with local region discrimination. Finally, the local network features inputs and public features expressing the local discriminant regions are concatenated for the pooling layer. A hash layer is added between the fully connected layer and the classification layer of the backbone network to generate high-quality hash codes. The final result is obtained by calculating the hash codes with the similarity metric.
Experimental results with the BT-MRI dataset showed that the proposed method can effectively identify tumor regions and more accurate hash codes can be generated by using the three loss functions in feature fusion. It has been demonstrated that the accuracy of medical image retrieval is effectively increased when our method is compared with existing image retrieval approaches.
Our method has demonstrated that the accuracy of medical image retrieval can be effectively increased and potentially applied to CADs.
脑肿瘤是一种致命疾病，可影响各年龄段人群。放射科医生在每年平均确诊的14000例脑肿瘤患者的早期诊断和治疗中发挥着关键作用。计算机辅助诊断系统（CAD）检测肿瘤的最佳方法是磁共振成像（MRI）。然而，由于大量图像的组织特性复杂，使用传统方法进行人工评估可能会导致一些不准确之处。因此，提出了一种精确医学图像哈希方法，该方法利用脑肿瘤的MRI图像结合可解释性和特征融合，以解决医学图像检索问题。
提出一种结合可解释性和特征融合的精确哈希方法，以解决在脑肿瘤MRI（BT-MRI）数据集上进行脑肿瘤检测时图像分辨率低的问题。首先，使用“对比学习”方法，用DenseNet201网络对数据集进行预训练。然后，创建一个全局网络，生成显著性图以产生具有局部区域区分的掩码裁剪。最后，将表示局部判别区域的局部网络特征输入和公共特征连接到池化层。在骨干网络的全连接层和分类层之间添加一个哈希层，以生成高质量的哈希码。通过使用相似性度量计算哈希码获得最终结果。
BT-MRI数据集的实验结果表明，所提出的方法能够有效识别肿瘤区域，并且通过在特征融合中使用三种损失函数可以生成更准确的哈希码。与现有图像检索方法相比，已证明我们的方法有效提高了医学图像检索的准确性。
我们的方法已证明可以有效提高医学图像检索的准确性，并有可能应用于CAD。
