Thalassemia is a heritable blood disorder which is the outcome of a genetic defect causing lack of production of hemoglobin polypeptide chains. However, there is less understanding of the precise frequency as well as sharing in these areas. Knowing about the frequency of thalassemia occurrence and dependable mutations is thus a significant step in preventing, controlling, and treatment planning. Here, Political Tangent Search Optimizer based Transfer Learning (PTSO_TL) is introduced for thalassemia detection. Initially, input data obtained from a particular dataset is normalized in the data normalization stage. Quantile normalization is utilized in the data normalization stage, and the data are then passed to the feature fusion phase, in which Weighted Euclidean Distance with Deep Maxout Network (DMN) is utilized. Thereafter, data augmentation is performed using the oversampling method to increase data dimensionality. Lastly, thalassemia detection is carried out by TL, wherein a convolutional neural network (CNN) is utilized with hyperparameters from a trained model such as Xception. TL is tuned by PTSO, and the training algorithm PTSO is presented by merging of Political Optimizer (PO) and Tangent Search Algorithm (TSA). Furthermore, PTSO_TL obtained maximal precision, recall, and f-measure values of about 94.3%, 96.1%, and 95.2%, respectively.

翻译：地中海贫血是一种遗传性血液疾病，由基因缺陷导致血红蛋白多肽链生成不足引起。然而，人们对这些地区的精确发病率和共享程度了解不足。因此，了解地中海贫血的发生频率和可靠突变是预防、控制和治疗规划中的重要步骤。本文提出了一种基于政治切线搜索优化器的迁移学习（PTSO_TL）方法用于地中海贫血检测。首先，从特定数据集获取的输入数据在数据归一化阶段进行归一化处理。数据归一化阶段采用分位数归一化，随后将数据传递到特征融合阶段，在该阶段中，使用加权欧氏距离与深度最大输出网络（DMN）。之后，采用过采样方法进行数据增强以增加数据维度。最后，通过迁移学习进行地中海贫血检测，其中利用卷积神经网络（CNN）结合预训练模型（如Xception）的超参数。迁移学习由PTSO调优，而训练算法PTSO是通过合并政治优化器（PO）和切线搜索算法（TSA）提出的。此外，PTSO_TL获得了约94.3%、96.1%和95.2%的最大精确率、召回率和F1值。