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All Journal Jurnal Penelitian Pendidikan IPA (JPPIPA)
Muhammad Fazly Qusyairy
Esa Unggul University
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Education Materials Science & Nanotechnology Physics

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Analisis Komparatif Metode Pengurangan Derau Klasik dan Pembelajaran Mendalam untuk Meningkatkan Kualitas Citra Parasit Malaria Wahyu Purnama Magribi; Habibullah Akbar; Muhammad Fazly Qusyairy; Tino Saputra; Eric Julianto; Decky Ryansyah
Jurnal Penelitian Pendidikan IPA Vol 12 No 4 (2026)
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.
Comparative Analysis of Convolutional Neural Network (ResNet-50) and Vision Transformer (ViT-B/16) for Histopathological Image Classification of Colorectal Cancer Muhammad Fazly Qusyairy; Habibullah Akbar; Wahyu Purnama Magribi; Khusnul Fajri Rhomadon
Jurnal Penelitian Pendidikan IPA Vol 12 No 7 (2026)
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v12i7.14880

Abstract

The diagnosis of colorectal cancer (CRC) through histopathological images requires high accuracy to support appropriate clinical decisions. Although Convolutional Neural Networks (CNN) have become the gold standard in medical image analysis, the emergence of Vision Transformer (ViT) architecture offers a new paradigm based on global attention mechanisms (self-attention) that is claimed to be superior on large-scale datasets. However, the effectiveness of ViT-B/16 on medical datasets with limited sample sizes and high texture variation remains debatable. This study aims to comprehensively evaluate the performance of the ViT-B/16 architecture compared to ResNet-50 on the NCT-CRC-HE-100K histopathology dataset, which consists of 9 network classes. The performance of both models was tested using equivalent training scenarios. The evaluation was conducted multidimensionally, covering classification metrics (Accuracy, F1-Score), training stability, feature space separability (t-SNE), visual interpretability (Grad-CAM), and computational efficiency. The experimental results show that ResNet-50 significantly outperforms ViT-B/16 with a test accuracy of 93.24%, compared to ViT-B/16 which only achieves 57.11%. The t-SNE analysis revealed that ViT-B/16 failed to form well-separated feature clusters due to a lack of inductive bias to recognize local features such as cell membrane edges. Failure analysis shows that ViT-B/16 often misclassifies adipose cells as mucus and smooth muscle as tumors. In terms of efficiency, ResNet-50 is 5.8 times lighter in storage size and has lower inference latency. This study concludes that CNN-based architecture (ResNet-50) is still far superior, more stable, and more feasible for clinical implementation than ViT-B/16 in the context of medium-scale histopathological image classification.
Co-Authors Akbar, Habibullah Decky Ryansyah Eric Julianto Khusnul Fajri Rhomadon Tino Saputra Wahyu Purnama Magribi