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Thalassemia is a heritable blood disorder which is the outcome of a geneticdefect causing lack of production of hemoglobin polypeptide chains. However,there is less understanding of the precise frequency as well as sharing inthese areas. Knowing about the frequency of thalassemia occurrence anddependable mutations is thus a significant step in preventing, controlling, andtreatment planning. Here, Political Tangent Search Optimizer based TransferLearning (PTSO_TL) is introduced for thalassemia detection. Initially, inputdata obtained from a particular dataset is normalized in the data normalizationstage. Quantile normalization is utilized in the data normalization stage, andthe data are then passed to the feature fusion phase, in which WeightedEuclidean Distance with Deep Maxout Network (DMN) is utilized. Thereafter, dataaugmentation is performed using the oversampling method to increase datadimensionality. Lastly, thalassemia detection is carried out by TL, wherein aconvolutional neural network (CNN) is utilized with hyperparameters from atrained model such as Xception. TL is tuned by PTSO, and the training algorithmPTSO is presented by merging of Political Optimizer (PO) and Tangent SearchAlgorithm (TSA). Furthermore, PTSO_TL obtained maximal precision, recall, andf-measure values of about 94.3%, 96.1%, and 95.2%, respectively.

Deep Maxout Network-based Feature Fusion and Political Tangent Search Optimizer enabled Transfer Learning for Thalassemia Detection