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All Journal ILKOM Jurnal Ilmiah Indonesian Journal of Data and Science Bioma : Jurnal Ilmiah Biologi
Felix Andika Dwiyanto
AGH University of Krak´ow
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

Introduction: Kidney diseases remain a major global health challenge, and computed tomography (CT) provides detailed renal imaging that supports accurate diagnosis. Although data augmentation is commonly used to improve deep learning performance on limited medical datasets, the effects of different augmentation strategies on image characteristics and model learning behavior remain insufficiently understood. Method: This study evaluated spatial, intensity, and hybrid augmentation for kidney CT image classification using a custom CNN, MobileNetV2, and EfficientNet-B0. A public dataset containing 12,446 CT images across Normal, Cyst, Tumor, and Stone classes was partitioned using stratified sampling into training, validation, and test sets. Four scenarios—baseline, spatial, intensity, and hybrid augmentation—were evaluated across three independent random seeds using accuracy, precision, recall, F1-score, and AUC. Results and Discussion: The baseline achieved mean accuracies of 99.96%, 98.32%, and 94.06% for CNN, MobileNetV2, and EfficientNet-B0, respectively. Intensity augmentation slightly improved CNN accuracy to 99.99% and consistently produced smaller performance degradation and more stable convergence than spatial and hybrid augmentation. Spatial and hybrid transformations generally reduced classification performance, indicating that excessive geometric changes may disrupt diagnostically relevant anatomical features. Conclusion: Baseline training provided the best overall performance, while intensity augmentation was the most effective augmentation strategy, demonstrating that preserving anatomically meaningful image characteristics is more important than indiscriminately increasing data diversity.
Lightweight Deep Learning Models for Lung Disease Classification Using Chest X-ray Images Abdullah Sholum; Aji Prasetya Wibawa; Ardhana Putra Agustavada; Dafa Fadhilah Hilmi; Felix Andika Dwiyanto
ILKOM Jurnal Ilmiah Vol 18, No 2 (2026)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v18i2.3351.302-319

Abstract

Lung disease classification from chest X-ray images using deep learning has attracted significant attention due to its potential to support rapid and automated medical diagnosis. However, many deep learning models require high computational resources, limiting their applicability in resource-constrained environments. This study evaluates the effectiveness of lightweight deep learning architectures for lung disease classification using chest X-ray images consisting of COVID-19, Normal, and Pneumonia classes. Three architectures were comparatively analyzed, including a baseline Convolutional Neural Network (CNN), EfficientNetV2, and MobileNetV2. All models were trained and evaluated under identical preprocessing and experimental conditions using a dataset of 697 chest X-ray images with a 60:20:20 split ratio for training, validation, and testing. Experimental results show that EfficientNetV2 and MobileNetV2 achieved classification accuracy up to 0.99 with AUC-ROC values approaching 1.0. In terms of computational efficiency, both lightweight architectures reduced trainable parameters by approximately 99.3% compared to the baseline CNN, decreasing from 576,851 to 3,843 trainable parameters through transfer learning with frozen pretrained layers. MobileNetV2 achieved this performance with 0.613 GFLOPs and a model size of 9,435 KB, while EfficientNetV2 required 0.781 GFLOPs and 15,993 KB. Although EfficientNetV2 demonstrated slightly more stable performance, MobileNetV2 provided the most effective trade-off between classification accuracy and computational efficiency, making it more suitable for deployment in resource-constrained healthcare environments. These findings demonstrate the feasibility of lightweight deep learning architectures for efficient and reliable lung disease classification from chest X-ray images
Co-Authors Abdullah Sholum Aji Prasetya Wibawa Ardha Ardhana Putra Agustavada Ardhana Putra Agustavada Dafa Fadhilah Hilmi