K Padmapriya, Periyathambi Ezhumalai
Department of Computer Science and Engineering, RMD Engineering College, Chennai, Tamil Nadu, India.
PeerJ Comput Sci. 2024 Dec 23;10:e2538. doi: 10.7717/peerj-cs.2538. eCollection 2024.
The best possible treatment planning and patient care depend on the precise diagnosis of brain diseases made with medical imaging information. Magnetic resonance imaging (MRI) is increasingly used in clinical score prediction and computer-aided brain disease (BD) diagnosis due to its outstanding correlation. Most modern collaborative learning methods require manually created feature representations for MR images. We present an effective iterative method and rigorously show its convergence, as the suggested goal is a non-smooth optimization problem that is challenging to tackle in general. In particular, we extract many image patches surrounding these landmarks by using data to recognize discriminative anatomical characteristics in MR images. Our experimental results, which demonstrated significant increases in key performance metrics with 500 data such as specificity of 94.18%, sensitivity of 93.19%, accuracy of 96.97%, F1-score of 94.18%, RMSE of 22.76%, and execution time of 4.875 ms demonstrated the efficiency of the proposed method, Deep Multi-Task Convolutional Neural Network (DMTCNN).
In this research present a DMTCNN for combined regression and classification. The proposed DMTCNN model aims to predict both the presence of brain diseases and quantitative disease-related measures like tumor volume or disease severity. Through cooperative learning of several tasks, the model might make greater use of shared information and improve overall performance. For pre-processing system uses an edge detector, which is canny edge detector. The proposed model learns many tasks concurrently, such as categorizing different brain diseases or anomalies, by extracting features from image patches using convolutional neural networks (CNNs). Using common representations across tasks, the multi-task learning (MTL) method enhances model generalization and diagnostic accuracy even in the absence of sufficient labeled data.
One of our unique discoveries is that, using our datasets, we verified that our proposed algorithm, DMTCNN, could appropriately categorize dissimilar brain disorders. Particularly, the proposed DMTCNN model achieves better than state-of-the-art techniques in precisely identifying brain diseases.
最佳的治疗方案规划和患者护理依赖于借助医学影像信息对脑部疾病进行精确诊断。由于磁共振成像（MRI）具有出色的相关性，其在临床评分预测和计算机辅助脑部疾病（BD）诊断中的应用日益广泛。大多数现代协作学习方法需要为磁共振图像手动创建特征表示。我们提出了一种有效的迭代方法，并严格证明了其收敛性，因为所提出的目标是一个非光滑优化问题，一般来说很难解决。特别是，我们通过使用数据来识别磁共振图像中的判别性解剖特征，提取了围绕这些地标的许多图像块。我们的实验结果表明，使用500个数据时，关键性能指标有显著提高，如特异性为94.18%、灵敏度为93.19%、准确率为96.97%、F1分数为94.18%、均方根误差为22.76%以及执行时间为4.875毫秒，这证明了所提出的深度多任务卷积神经网络（DMTCNN）方法的有效性。
在本研究中，我们提出了一种用于联合回归和分类的DMTCNN。所提出的DMTCNN模型旨在预测脑部疾病的存在以及与疾病相关的定量指标，如肿瘤体积或疾病严重程度。通过对多个任务的协同学习，该模型可能会更多地利用共享信息并提高整体性能。对于预处理系统，使用了一种边缘检测器，即Canny边缘检测器。所提出的模型通过使用卷积神经网络（CNN）从图像块中提取特征，同时学习多个任务，例如对不同的脑部疾病或异常进行分类。通过跨任务使用共同的表示，多任务学习（MTL）方法即使在没有足够标记数据的情况下也能提高模型的泛化能力和诊断准确性。
我们的独特发现之一是，使用我们的数据集，我们验证了所提出的算法DMTCNN能够恰当地对不同的脑部疾病进行分类。特别是，所提出的DMTCNN模型在精确识别脑部疾病方面比现有技术表现更好。
