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Andriyani, Annisa Divayu
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Improved Stroke Classification Accuracy by Using Hybrid Inception and Xception Models Atikananda, Desta; Riyadi, Slamet; Damarjati, Cahya; Andriyani, Annisa Divayu
Scientific Journal of Informatics Vol. 12 No. 4: November 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i4.29023

Abstract

Purpose: Stroke is one of the leading causes of death and disability in the world that requires a fast and accurate diagnosis system. A major challenge in classifying strokes using deep learning is data imbalances, where the number of stroke patients is much less than that of non-stroke patients. Methods/Study design/approach: This research proposes a Hybrid model approach that combines Inception and Xception architectures, and applies Synthetic Minority Over-sampling Technique (SMOTE) to balance the data distribution. The dataset used consisted of 5,110 entries with 12 stroke risk features, and evaluation was performed using accuracy, precision, recall, and F1-score metrics. Result/Findings: The results show that the Hybrid model provides the best performance with an accuracy of 92.2%, outperforming the Inception (86.28%) and Xception (89.26%) models. In addition, the Hybrid model showed high and balanced precision and recall values, reflecting its reliability in detecting stroke cases. Novelty/Originality/Value: The novelty of this research lies in combining the multi-scale feature extraction power of the Inception architecture and the depthwise separable convolution efficiency of the Xception architecture in a hybrid model. This approach is proven to excel in tabular data-based stroke classification and has the potential to be applied in automated medical diagnosis systems.
Co-Authors Atikananda, Desta Cahya Damarjati Slamet Riyadi