Abstract
Thyroid classification 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-fitting 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 efficient thyroid classification 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 classification performance. The Transfer learning method selects the optimal batch size for the Bi-LSTM model to eliminate the overfitting 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 significant features in the dataset are selected by applying a modified firefly algorithm (MFA). The modified firefly 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 classifier’s effectiveness. The evaluation results display that the proposed novel approach successfully identifies and classifies thyroid problems using fetal brain magnetic resonance imaging (MRI) images of various Gestational weeks.
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All agreed on the content of the study. GJ and MSA collected all the data for analysis. GK agreed on the methodology. GJ and MSA completed the analysis based on agreed steps. Results and conclusions are discussed and written together. The author read and approved the final manuscript.
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Keerthi, G., Abirami, M.S. MFO-TL: modified firefly optimal transfer learning based motion correction of fetal brain and placenta MRI for thyroid prediction. Evolving Systems 15, 361–374 (2024). https://doi.org/10.1007/s12530-023-09556-3
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DOI: https://doi.org/10.1007/s12530-023-09556-3