  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.
