Alshardan Amal, Alruwais Nuha, Alqahtani Hamed, Alshuhail Asma, Almukadi Wafa Sulaiman, Sayed Ahmed
Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia.
Department of Computer Science and Engineering, College of Applied Studies and Community Services, King Saud University, Saudi Arabia, P.O.Box 22459, Riyadh, 11495, Saudi Arabia.
Sci Rep. 2024 Dec 18;14(1):30549. doi: 10.1038/s41598-024-81966-y.
Recognition and segmentation of brain tumours (BT) using MR images are valuable and tedious processes in the healthcare industry. Earlier diagnosis and localization of BT provide timely options to select effective treatment plans for the doctors and can save lives. BT segmentation from Magnetic Resonance Images (MRI) is considered a big challenge owing to the difficulty of BT tissues, and segmenting them from the healthier tissue is challenging when manual segmentation is done through radiologists. Among the recent proposals for the brain segmentation method, the BT segmentation method based on machine learning (ML) and image processing could be better. Thus, the DL-based brain segmentation method is extensively applied, and the convolutional network has better brain segmentation effects. The deep convolutional network model has the problem of a large loss of information and a large number of parameters in the encoding and decoding processes. With this motivation, this article presents a new Deep Transfer Learning with Semantic Segmentation based Medical Image Analysis (DTLSS-MIA) technique on MRI images. The DTLSS-MIA technique aims to segment the affected BT area in the MRI images. At first, the presented method utilizes a Median filtering (MF) approach to optimize the quality of MRI images and remove the noise. For the semantic segmentation method, the DTLSS-MIA method follows DeepLabv3 + with a backbone of the EfficientNet model for determining the affected brain region. Moreover, the CapsNet architecture is employed for the feature extraction process. Lastly, the crayfish optimization (CFO) technique with diffusion variational autoencoder (D-VAE) architecture is used as a classification mechanism, and the CFO technique effectively tunes the D-VAE hyperparameter. The simulation analysis of the DTLSS-MIA technique is validated on a benchmark dataset. The performance validation of the DTLSS-MIA technique exhibited a superior accuracy value of 99.53% over other methods.
利用磁共振图像对脑肿瘤（BT）进行识别和分割，在医疗行业中是有价值但繁琐的过程。脑肿瘤的早期诊断和定位为医生选择有效的治疗方案提供了及时的选择，并且可以挽救生命。由于脑肿瘤组织的复杂性，从磁共振图像（MRI）中分割脑肿瘤被认为是一项巨大的挑战，当通过放射科医生进行手动分割时，将它们与健康组织区分开来具有挑战性。在最近提出的脑部分割方法中，基于机器学习（ML）和图像处理的脑肿瘤分割方法可能会更好。因此，基于深度学习（DL）的脑部分割方法被广泛应用，并且卷积网络具有更好的脑部分割效果。深度卷积网络模型在编码和解码过程中存在信息大量丢失和参数众多的问题。基于此动机，本文提出了一种新的基于深度迁移学习与语义分割的医学图像分析（DTLSS-MIA）技术，用于处理MRI图像。DTLSS-MIA技术旨在分割MRI图像中受影响的脑肿瘤区域。首先，所提出的方法利用中值滤波（MF）方法来优化MRI图像的质量并去除噪声。对于语义分割方法，DTLSS-MIA方法采用带有EfficientNet模型骨干的DeepLabv3 +来确定受影响的脑区。此外，CapsNet架构用于特征提取过程。最后，采用带有扩散变分自编码器（D-VAE）架构的小龙虾优化（CFO）技术作为分类机制，并且CFO技术有效地调整D-VAE超参数。DTLSS-MIA技术的仿真分析在一个基准数据集上得到了验证。DTLSS-MIA技术的性能验证显示出比其他方法更高的准确率，达到了99.53%。
