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所致。然而，关于这些地区的精确发病率和共享情况认知尚不充分。因此，明确地中海贫血的发生频率及可靠的突变信息，对于预防、控制和治疗规划具有重要意义。本文提出基于政治切线搜索优化器的迁移学习（PTSO_TL）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标准化阶段进行归一化处理，采用分位数归一化方法；随后数据进入特征融合阶段，使用加权欧氏距离与深度最大输出网络（DMN）。此后，通过过采样方法进行数据增强以提升数据维度。最后，利用迁移学习（TL）执行地中海贫血检测，其中采用卷积神经网络（CNN）并结合预训练模型（如Xception）的超参数。TL通过PTSO进行调优，该训练算法由政治优化器（PO）和切线搜索算法（TSA）融合而成。实验结果表明，PTSO_TL获得了约94.3%、96.1%和95.2%的最高精确率、召回率和F1值。