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Magribi, Wahyu Purnama
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Education Materials Science & Nanotechnology Physics

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Explainable Brain Tumor Classification Using EfficientNet-B2 and Grad-CAM on MRI Dataset　 Saputra, Tino; Magribi, Wahyu Purnama; Tundjungsari, Vitri
Jurnal Penelitian Pendidikan IPA Vol 12 No 3 (2026): In Progress
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v12i3.14179

Abstract

Brain tumors are life-threatening central nervous system disorders requiring early and accurate diagnosis for effective clinical management. Although MRI is the standard modality for detection, manual interpretation remains prone to inconsistency, particularly for complex cases such as glioma. This study proposes an explainable deep learning framework integrating EfficientNet-B2 with a threshold-based two-stage classification scheme and Grad-CAM interpretability analysis. In the first stage, a one-versus-rest binary classifier with an optimized threshold (τ = 0.20) performs glioma detection; the second stage classifies remaining cases into meningioma, pituitary tumor, or normal. The dataset comprises 7,023 MRI images across four classes from a public Kaggle repository. Preprocessing includes CLAHE contrast enhancement, normalization, and augmentation. EfficientNet-B0 serves as the baseline. EfficientNet-B2 achieves 97.9% overall accuracy, outperforming the baseline (96.7%), with a glioma F1-score of 0.988 at the optimal threshold. Grad-CAM visualizations confirm the model focuses on anatomically relevant regions, enhancing transparency and clinical trustworthiness. The proposed framework demonstrates that combining architectural capacity, threshold-based inference, and explainability yields a reliable system for computer-aided brain tumor diagnosis.
Analisis Komparatif Metode Pengurangan Derau Klasik dan Pembelajaran Mendalam untuk Meningkatkan Kualitas Citra Parasit Malaria Magribi, Wahyu Purnama; Akbar, Habibullah; Qusyairy, Muhammad Fazly; Saputra, Tino; Julianto, Eric; Ryansyah, Decky
Jurnal Penelitian Pendidikan IPA Vol 12 No 4 (2026): In Progress
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v12i4.14840

Abstract

Malaria diagnosis accuracy depends on microscopic image quality, often compromised by noise. This study comprehensively evaluates classical denoising (morphological, median, bilateral filters) against deep learning architectures (DnCNN, Autoencoder, U-Net) for malaria parasite images. Using the Cell Images for Detecting Malaria dataset with synthetic Gaussian, salt-and-pepper, and mixed noise, experiments measured PSNR, SSIM, and processing time. Results indicate U-Net achieved superior performance (PSNR 36.69 dB, SSIM 0.9577), significantly outperforming Autoencoder (PSNR 26.12 dB) and classical methods (PSNR 23.14 dB). The baseline DnCNN architecture did not achieve competitive performance (PSNR 8.42 dB), indicating that domain-specific parameter tuning and data normalization adjustments are necessary for effective application to microscopic imaging. Autoencoder demonstrated the highest computational efficiency (1.64 ms per image), though the 10.57 dB PSNR gap relative to U-Net suggests that the quality trade-off may limit its suitability in accuracy-critical diagnostic scenarios. U-Net best preserved morphological details crucial for diagnosis and is recommended as the primary choice for malaria diagnostic systems prioritizing accuracy, while Autoencoder represents the most computationally efficient alternative for resource-constrained deployment. These findings support developing robust computer-aided diagnosis systems and contribute a comprehensive quantitative benchmark for denoising methods in malaria microscopy.
Co-Authors Akbar, Habibullah Julianto, Eric Qusyairy, Muhammad Fazly Ryansyah, Decky Saputra, Tino Vitri Tundjungsari