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. 2025 Apr 10;15(1):12334.
doi: 10.1038/s41598-025-97034-y.

Enhancing neurological disease diagnostics: fusion of deep transfer learning with optimization algorithm for acute brain stroke prediction using facial images

Fadwa Alrowais  1 , Mohammed Alqahtani  2 , Jahangir Khan  3 , Achraf Ben Miled  4 , Da'ad Albalawneh  5 , Abdulwhab Alkharashi  6 , Samah Al Zanin  7 , Radwa Marzouk  8
Affiliations

Enhancing neurological disease diagnostics: fusion of deep transfer learning with optimization algorithm for acute brain stroke prediction using facial images

Fadwa Alrowais et al. Sci Rep. .

Abstract

Stroke is a main risk to life and fitness in current society, particularly in the aging population. Also, the stroke is recognized as a cerebrovascular accident. It contains a nervous illness, which can result from haemorrhage or ischemia of the brain veins, and regular mains to assorted motor and cognitive damages that cooperate with functionality. Screening for stroke comprises physical examination, history taking, and valuation of risk features like age or certain cardiovascular illnesses. Symptoms and signs of stroke include facial weakness. Even though computed tomography (CT) and magnetic resonance imaging (MRI) are standard diagnosis techniques, artificial intelligence (AI) systems have been constructed based on these methods, which deliver fast detection. AI is gaining high attention and is being combined into numerous areas with medicine to enhance the accuracy of analysis and the quality of patient care. This paper proposes an enhancing neurological disease diagnostics fusion of transfer learning for acute brain stroke prediction using facial images (ENDDFTL-ABSPFI) method. The proposed ENDDFTL-ABSPFI method aims to enhance brain stroke detection and classification models using facial imaging. Initially, the image pre-processing stage applies the fuzzy-based median filter (FMF) model to eliminate the noise in input image data. Furthermore, fusion models such as Inception-V3 and EfficientNet-B0 perform the feature extraction. Moreover, the hybrid of convolutional neural network and bidirectional long short-term memory (CNN-BiLSTM) model is employed for the brain stroke classification process. Finally, the multi-objective sailfish optimization (MOSFO)-based hyperparameter selection process is carried out to optimize the classification outcomes of the CNN-BiLSTM model. The simulation validation of the ENDDFTL-ABSPFI technique is investigated under the Kaggle dataset concerning various measures. The comparative evaluation of the ENDDFTL-ABSPFI technique portrayed a superior accuracy value of 98.60% over existing methods.

Keywords: Acute brain stroke prediction; Facial images; Fusion of transfer learning; Image pre-processing; Optimization algorithm.

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Conflict of interest statement

Declarations. Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Workflow of ENDDFTL-ABSPFI method.
Fig. 2
Fig. 2
Architecture of CNN-BiLSTM model.
Fig. 3
Fig. 3
Sample images of (a) no stroke and (b) stroke.
Fig. 4
Fig. 4
80%TRPH and 20%TSPH of (a, b) confusion matrix, (c, d) curve of PR and ROC.
Fig. 5
Fig. 5
Average of ENDDFTL-ABSPFI method below 80%TRPH and 20%TSPH.
Fig. 6
Fig. 6
formula image curve of ENDDFTL-ABSPFI technique below 80%TRPH and 20%TSPH.
Fig. 7
Fig. 7
Loss curve of ENDDFTL-ABSPFI technique under 80%TRPH and 20%TSPH.
Fig. 8
Fig. 8
70%TRPH and 30%TSPH of (a, b) confusion matrix, (c, d) curve of PR and ROC.
Fig. 9
Fig. 9
Average of ENDDFTL-ABSPFI model below 70%TRPH and 30%TSPH.
Fig. 10
Fig. 10
formula image curve of ENDDFTL-ABSPFI model below 70%TRPH and 30%TSPH.
Fig. 11
Fig. 11
Loss curve of ENDDFTL-ABSPFI model below 70%TRPH and 30%TSPH.
Fig. 12
Fig. 12
Comparative analysis of ENDDFTL-ABSPFI methodology with existing models.
Fig. 13
Fig. 13
CT analysis of ENDDFTL-ABSPFI approach with existing methods.
Fig. 14
Fig. 14
Result analysis of the ablation study of ENDDFTL-ABSPFI technique.
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References

    1. Sirsat, M. S., Fermé, E. & Camara, J. Machine learning for brain stroke: A review. J. Stroke Cerebrovasc. Dis.29(10), 105162 (2020). - PubMed
    1. AL-Nuaimi, B. T., Suhail, R. A. & El-kenawy, E. S. M. Adaptive feature selection based on machine learning algorithms for lung tumors diagnosis and the COVID-19 index. J. Intell. Syst. Internet Things11(2), 24 (2024).
    1. Islam, M. S., Hussain, I., Rahman, M. M., Park, S. J. & Hossain, M. A. Explainable artificial intelligence model for stroke prediction using EEG signal. Sensors22(24), 9859 (2022). - PMC - PubMed
    1. Hilbert, A. et al. Data-efficient deep learning of radiological image data for outcome prediction after endovascular treatment of patients with acute ischemic stroke. Comput. Biol. Med.115, 103516 (2019). - PubMed
    1. Choi, Y. A. et al. Deep learning-based stroke disease prediction system using real-time bio signals. Sensors21(13), 4269 (2021). - PMC - PubMed

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