Pandiaraj A, Kshirsagar Pravin R, Thiagarajan R, Tak Tan Kuan, Sivaneasan B
Department of Computing Technologies, School of Computing, SRM Institute of Science and Technology, Kattankulathur, Chennai, Tamil Nadu, 603203, India.
J D College of Engineering & Management, Nagpur, Maharashtra, 441501, India.
J Imaging Inform Med. 2024 Dec 4. doi: 10.1007/s10278-024-01311-7.
The highly contagious malaria disease is spread by the female Anopheles mosquito. This disease results in a patient's death or incapacity to move their muscles, if it is not appropriately identified in the early stages. A Rapid Diagnostic Test (RDT) is a frequently used approach to find malaria cells in red blood cells. However, it might not be able to identify infections with small amounts of samples. In the microscopic detection model, blood stains are placed under a microscope for diagnosing malaria. But accurate diagnosis is hard in this method, particularly in developing nations where the disease is most common. The microscopic detection processes are expensive and time-consuming due to the usage of microscopes. The quality of the blood smears and the availability of a qualified specialist, who is skilled in recognizing the disease, impact the accuracy of malaria detection results. The traditional deep learning-based malaria identification models need more processing power. Therefore, a deep learning-based adaptive method is designed to detect malaria cells through the medical image. Hence, the images are gathered from the standard sites and then fed to the segmentation process. Here, the abnormality segmentation is carried out with the help of a developed Trans-MobileUNet + + (T-MUnet + +) network. Trans-MobileUNet + + captures global context, so it is well-suited for segmentation tasks. The segmented image is applied to the adaptive detection phase where the Adaptive and Atrous Convolution-based Recurrent MobilenetV2 (AA-CRMV2) model is designed for the effective recognition of malaria cells. The efficiency of the designed approach is elevated by optimizing the parameters from the AA-CRMV2 network with the help of the Updated Random Parameter-based Fennec Fox Optimization (URP-FFO) algorithm. Several experimental analyses are evaluated in the implemented model over classical techniques to display their effectualness rate.
高传染性的疟疾由雌性按蚊传播。如果在早期阶段未得到适当识别，这种疾病会导致患者死亡或肌肉无法活动。快速诊断测试（RDT）是在红细胞中查找疟原虫细胞的常用方法。然而，它可能无法识别少量样本中的感染情况。在显微镜检测模型中，将血涂片置于显微镜下以诊断疟疾。但这种方法难以进行准确诊断，尤其是在疟疾最为常见的发展中国家。由于使用显微镜，显微镜检测过程既昂贵又耗时。血涂片的质量以及具备识别该疾病技能的合格专家的可用性，都会影响疟疾检测结果的准确性。传统的基于深度学习的疟疾识别模型需要更强的处理能力。因此，设计了一种基于深度学习的自适应方法，通过医学图像来检测疟原虫细胞。于是，从标准站点收集图像，然后将其输入到分割过程中。在这里，借助已开发的Trans-MobileUNet ++（T-MUnet ++）网络进行异常分割。Trans-MobileUNet ++能够捕捉全局上下文，因此非常适合分割任务。将分割后的图像应用于自适应检测阶段，在此阶段，基于自适应空洞卷积的循环MobileNetV2（AA-CRMV2）模型被设计用于有效识别疟原虫细胞。借助基于更新随机参数的耳廓狐优化（URP-FFO）算法对AA-CRMV2网络的参数进行优化，从而提高了所设计方法的效率。在已实现的模型中，针对经典技术进行了多项实验分析，以展示其有效率。
