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中存在很多计算成本高、准确率低的问题。基于深度学习的优化模型利用 CT 图像利用 RNA 基因组预测肝癌，利用 NGS 预测基因组分类在最近的医学疾病分类中可能性更高。本文提出了一种混合深度学习技术构造与 SegNet、MultiResUNet 和磷虾群优化 (KHO) 算法，以执行优化技术用于深度学习方法的肝脏病变提取和 RNA 测序。所提出的技术实现了 SegNet，用于从 CT 扫描中分离肝脏和基因组；MultiResUNet 用于执行肝脏病变的提取。KHO 算法与深度学习方法相结合，为每个卷积神经网络模型调整超参数，并增强了分割过程，可以精细地识别导致肝脏分类疾病的序列。将所提出的技术与基因组中 NGS 肝脏病变分类 (LL) 的相关技术进行比较。性能结果表明，所提出的技术在各种性能指标上优于其他算法。将所提出的技术与基因组中 NGS 肝脏病变分类 (LL) 的相关技术进行比较。性能结果表明，所提出的技术在各种性能指标上优于其他算法。将所提出的技术与基因组中 NGS 肝脏病变分类 (LL) 的相关技术进行比较。性能结果表明，所提出的技术在各种性能指标上优于其他算法。