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All Journal Indonesian Journal of Data and Science Bioma : Jurnal Ilmiah Biologi
Ardha Ardhana Putra Agustavada
Universitas Negeri Malang
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Computer Science & IT Decision Sciences, Operations Research & Management Education Immunology & microbiology Mathematics

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MORPHOLOGICAL CHARACTERIZATION OF BRAIN TUMOR TISSUE IN MRI IMAGES USING CNN AND TRANSFER LEARNING Dafa Fadhilah Hilmi; Aji Prasetya Wibawa; Ardha Ardhana Putra Agustavada; Abdullah Sholum; Felix Andika Dwiyanto
BIOMA : Jurnal Ilmiah Biologi Vol. 15 No. 1 (2026): April 2026
Publisher : Prodi Pendidikan Biologi, FPMIPATI, Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/bioma.v15i1.3550

Abstract

This study evaluates the role of computational pattern recognition as an observational method for analyzing morphological characteristics of brain tumor tissue in MRI data. A total of 6,056 labeled MRI images, including glioma, meningioma, and pituitary tumor cases, were examined. The images were standardized to maintain uniform structural representation and processed using three convolutional-based architectures: a baseline CNN, MobileNetV2, and EfficientNet-B0. Model performance was assessed using accuracy, precision, recall, F1-score, AUC-ROC, and a confusion matrix. The findings show variation in identification performance across tumor categories, with pituitary tumors consistently recognized, while misclassification predominantly occurred between glioma and meningioma. Models based on transfer learning achieved stronger agreement with the reference labels than the baseline CNN, with MobileNetV2 demonstrating the most stable performance. The recurrence of similar misclassification patterns across models suggests the presence of shared morphological characteristics in MRI representations of certain tumor types. Overall, the results support the use of computational image analysis as a structured observational framework that enables consistent evaluation of brain tumor tissue morphology in MRI, providing complementary insights for biological interpretation.
Effect of Spatial, Intensity, and Hybrid Augmentation on Kidney CT Image Classification Ardha Ardhana Putra Agustavada; Aji Prasetya Wibawa; Dafa Fadhilah Hilmi; Abdullah Sholum; Felix Andika Dwiyanto
Indonesian Journal of Data and Science Vol. 7 No. 2 (2026): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v7i2.443

Abstract

Kidney diseases remain a significant global health challenge, and computed tomography (CT) plays an important role in supporting their diagnosis through detailed visualization of renal structures. In deep learning-based medical image classification, data augmentation is commonly employed to mitigate the limitations of small training datasets; nevertheless, the effects of distinct augmentation strategies on image characteristics and learning behavior across architectures remain insufficiently explored. This study investigates the impact of spatial, intensity, and hybrid augmentation on kidney CT image classification using CNN, MobileNetV2, and EfficientNet-B0 architectures. A stratified data split was adopted, and all experiments were repeated using three random seeds (1, 42, and 123), with results reported as mean ± standard deviation. Four training scenarios (baseline, spatial, intensity, and hybrid augmentation) were evaluated using accuracy, precision, recall, F1-score, and Area Under the Curve (AUC). The baseline configuration achieved the highest overall performance, reaching mean accuracies of 99.96 ± 0.04%, 98.32 ± 0.06%, and 94.06 ± 0.58% for CNN, MobileNetV2, and EfficientNet-B0, respectively. Among the augmentation strategies, intensity augmentation achieved the highest performance only for CNN while consistently exhibiting smaller performance degradation relative to the baseline and more stable convergence than the spatial and hybrid augmentation approaches. The consistently high baseline performance, particularly for CNN, underscores the importance of rigorous validation on public medical imaging datasets. These findings indicate that preserving anatomically relevant image characteristics is more beneficial than indiscriminately increasing data diversity. Therefore, while the baseline remained the best overall training strategy, intensity augmentation was the most effective augmentation approach
Co-Authors Abdullah Sholum Aji Prasetya Wibawa Dafa Fadhilah Hilmi Felix Andika Dwiyanto