MFO‑TL: modifed frefy optimal transfer learning based motion correction of fetal brain and placenta MRI for thyroid prediction

  Thyroid classifcation is required in the medical domain to better assist doctors in deciding diagnostic treatments. Although
many researchers conducted experiments to detect abnormal conditions of fetal brains in an earlier stage, there exist several
limitations like over-ftting problems and imbalance data problems. To deal with these problems, this paper proposes ‘Transfer learning- Bidirectional Long Short Term Memory (TL-BiLSTM) which is an efcient thyroid classifcation model. This
paper focuses on identifying the defects in fetal brains in a primary stage by investigating the thyroid range of the mother
during the 19th week of pregnancy. In this research, TL is applied with Bi-LSTM for the improvement of Thyroid classifcation performance. The Transfer learning method selects the optimal batch size for the Bi-LSTM model to eliminate
the overftting problem. The bi-LSTM model learns the sequence in forward and reverses mode to store the useful features
for the long term and discard the irrelevant features. The most signifcant features in the dataset are selected by applying a
modifed frefy algorithm (MFA). The modifed frefy algorithm has the advantages of easy escape from local optima and a
good convergence rate. For evaluation purposes, the thyroid dataset is used as input for investigating the proposed classifer’s
efectiveness. The evaluation results display that the proposed novel approach successfully identifes and classifes thyroid
problems using fetal brain magnetic resonance imaging (MRI) images of various Gestational weeks