Department of Information Technology, Karpagam College of Engineering, Coimbatore, TamilNadu, India.
Department of CSE, Sri Krishna College of Engineering and Technology, Coimbatore, TamilNadu, India.
Biosystems. 2020 Nov;197:104211. doi: 10.1016/j.biosystems.2020.104211. Epub 2020 Aug 11.
The conventional image segmentation techniques have a lot of issues with highest computational cost and low level accuracy for medical image diagnosis and genome analysis. The deep learning based optimization models utilize to predict the liver cancer with RNA genome using CT images and the prediction of genome classification with NGS is a higher probable in recent medical disease classification. This paper proposes a hybrid deep learning technique constructs with SegNet, MultiResUNet, and Krill Herd optimization (KHO) algorithm to perform the extraction of the liver lesions and RNA sequencing that the optimization techniques used into the deep learning method. The proposed technique implements the SegNet for segregating the liver with genome from the CT scan; the MultiResUNet is constructed to perform the extractions of liver lesions. The KHO algorithm is combined with the deep learning approaches for tuning the hyper parameters to every Convolutional neural network model and enhances the segmentation process which may elaborately identifies the sequence that causes the liver classification disease. The proposed technique is compared with the related techniques on liver lesion classification (LL) for NGS in genome. The performance results show that the proposed technique is better to other algorithms on various performance metrics.
传统的图像分割技术在医学图像诊断和基因组分析方面存在计算成本高和准确率低的问题。基于深度学习的优化模型被用于利用 CT 图像预测肝癌和使用 NGS 预测基因组分类，这在最近的医学疾病分类中是一种更有可能的方法。本文提出了一种混合深度学习技术，该技术由 SegNet、MultiResUNet 和 Krill Herd 优化（KHO）算法构建，用于提取肝脏病变和 RNA 测序，该优化技术应用于深度学习方法中。所提出的技术实现了 SegNet 用于从 CT 扫描中分离带有基因组的肝脏；构建了 MultiResUNet 来提取肝脏病变。KHO 算法与深度学习方法相结合，用于调整每个卷积神经网络模型的超参数，并增强分割过程，以便更精细地识别导致肝脏分类疾病的序列。该技术在基因组中的 NGS 肝脏病变分类（LL）方面与相关技术进行了比较。性能结果表明，该技术在各种性能指标上均优于其他算法。
