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.

用于地中海贫血检测的政治切线搜索优化器基于迁移学习 (PTSO_TL) 在正常化数据、特征融合、数据增强和训练调优等阶段的整合应用中，达到了约 94.3%、96.1% 和 95.2% 的最大精度、召回率和 F 值。

基于深度 Maxout 网络特征融合与政治切线搜索优化的转移学习用于地中海贫血检测

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

Thalassemia is a group of inherited blood disorders that happen when
hemoglobin, the protein in red blood cells that carries oxygen, is not made
enough. It is found all over the body and is needed for survival. If both
parents have thalassemia, a child's chance of getting it increases. Genetic
counselling and early diagnosis are essential for treating thalassemia and
stopping it from being passed on to future generations. It may be hard for
healthcare professionals to differentiate between people with thalassemia
carriers and those without. The current blood tests for beta thalassemia
carriers are too expensive, take too long, and require too much screening
equipment. The World Health Organization says there is a high death rate for
people with thalassemia. Therefore, it is essential to find thalassemia
carriers to act quickly. High-performance liquid chromatography (HPLC), the
standard test method, has problems such as cost, time, and equipment needs. So,
there must be a quick and cheap way to find people carrying the thalassemia
gene. Using federated learning (FL) techniques, this study shows a new way to
find people with the beta-thalassemia gene. FL allows data to be collected and
processed on-site while following privacy rules, making it an excellent choice
for sensitive health data. Researchers used FL to train a model for
beta-thalassemia carriers by looking at the complete blood count results and
red blood cell indices. The model was 92.38 % accurate at telling the
difference between beta-thalassemia carriers and people who did not have the
disease. The proposed FL model is better than other published methods in terms
of how well it works, how reliable it is, and how private it is. This research
shows a promising, quick, accurate, and low-cost way to find thalassemia
carriers and opens the door for screening them on a large scale.

使用联邦学习技术，本研究提出了一种新的、快速、准确和低成本的方法来检测地中海贫血基因携带者，该方法通过分析完整的血液计数结果和红细胞指数来训练一个模型，92.38% 的准确率能区分患有该疾病的贝塔地中海贫血携带者和正常人，相比使用高效液相色谱法等其他已发布的方法，该 FL 模型的性能更好、更可靠且更保护个人隐私

利用联邦学习技术进行 β 地中海贫血携带者检测

Beta Thalassemia Carriers detection empowered federated Learning

Owing to the invasiveness and low accuracy of other tests, including biopsy
and ferritin levels, magnetic resonance imaging (T2 and T2*-MRI) has been
considered the standard test for patients with thalassemia (THM). Regarding
deep learning networks in medical sciences for improving diagnosis and
treatment purposes and the existence of minimal resources for them, we decided
to provide a set of magnetic resonance images of the cardiac and hepatic
organs. The dataset included 124 patients (67 women and 57 men) with a THM age
range of (5-52) years. In addition, patients were divided into two groups: with
follow-up (1-5 times) at time intervals of about (5-6) months and without
follow-up. Also, T2* and, R2* values, the results of the cardiac and hepatic
report (normal, mild, moderate, severe, and very severe), and laboratory tests
including Ferritin, Bilirubin (D, and T), AST, ALT, and ALP levels were
provided as an Excel file. This dataset CHMMOTv1) has been published in
Mendeley Dataverse and is accessible through the web at: this http URL

This paper presents a dataset consisting of MRI images, laboratory tests, and clinical reports of patients with thalassemia, which can be used for medical diagnosis and treatment purposes, especially with the application of deep learning networks.