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合成不足而引起。然而，目前对这些地区的确切发病率及其分布情况了解不足。因此，掌握地中海贫血的发生频率及可靠突变信息，对于疾病预防、控制和治疗规划具有重要意义。本文提出了一种基于政治正切搜索优化器的迁移学习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标准化处理。该阶段采用分位数归一化方法，随后数据被传递至特征融合阶段，该阶段采用加权欧氏距离与深度最大输出网络进行特征融合。接着，使用过采样方法进行数据增强以提升数据维度。最后，通过迁移学习进行地中海贫血检测，其中采用卷积神经网络并借鉴了Xception等预训练模型的超参数。迁移学习过程通过政治正切搜索优化器进行调优，该训练算法融合了政治优化器和正切搜索算法。实验表明，所提出的方法获得了约94.3%的精确率、96.1%的召回率和95.2%的F1值。