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Roslan, Nur Syahirah
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Earth & Planetary Sciences Environmental Science Materials Science & Nanotechnology Mathematics Physics

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Optimized transfer learning for detection susceptibility vessel sign in stroke using gorilla troops optimizer Albashah, Nur Lyana Shahfiqa Lyana; Faye, Ibrahima; Roslan, Nur Syahirah; Bakar, Rohani; Muslim, Norliana
International Journal of Advances in Applied Sciences Vol 14, No 4: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v14.i4.pp1040-1049

Abstract

The blockage of blood vessels causes ischemic stroke due to clots. The susceptibility vessel sign (SVS), observed through susceptibility-weighted imaging (SWI) via magnetic resonance imaging (MRI), is a key indicator that reveals clots within brain vessels. Early detection of these clots is crucial for timely and effective treatment. Image-based detection methods, particularly non-invasive techniques like MRI, offer a superior approach compared to other modalities. This study proposes an optimized method using transfer learning to classify SVS. The deep convolutional neural network (DCNN) residual network 50 version 2 (ResNet50V2) was applied for classification, with hyperparameters fine-tuned using the gorilla troops optimizer (GTO). The optimized proposed model achieved an accuracy of 94%, sensitivity of 100%, specificity of 88%, and an F1-score of 93%. This significantly outperforms the standard ResNet50V2 model using the default parameter, which achieved an accuracy of 91%, sensitivity of 82%, specificity of 100%, and an F1-score of 77%. These results demonstrate that the proposed method significantly enhances the detection of SVS, offering a promising tool for early ischemic stroke diagnosis.
Co-Authors Albashah, Nur Lyana Shahfiqa Lyana Bakar, Rohani Faye, Ibrahima Muslim, Norliana