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All Journal JOURNAL OF SCIENCE AND SOCIAL RESEARCH JOURNAL OF ICT APLICATIONS AND SYSTEM
Andri Swandi
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Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Social Sciences

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Enhanced Classification of Brain MRI Images for Tumor Detection Using Transfer Learning and Grad-CAM-Based Explainable Convolutional Neural Network (CNN) Putra, Irwandi Rizki; Zulrahmadi; Andri Swandi; Yulia; Tasya Destria Putri
Journal of ICT Applications System Vol 4 No 2 (2025): Journal of ICT Aplications and System
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56313/jictas.v4i2.454

Abstract

Accurate and explainable classification of brain Magnetic Resonance Imaging (MRI) is crucial for the early detection and treatment of brain tumors. This study introduces an enhanced deep learning framework that integrates transfer learning with Grad-CAM-based explainable Convolutional Neural Network (CNN) for tumor classification. The proposed approach utilizes a fine-tuned EfficientNet-B0 architecture with an optimized preprocessing pipeline consisting of Contrast Limited Adaptive Histogram Equalization (CLAHE), normalization, and multi-variant augmentation (rotation, flipping, and zoom). The model was trained on a publicly available brain MRI dataset comprising 3,000 images classified into four categories: glioma, meningioma, pituitary tumor, and non-tumor. Evaluation metrics include accuracy, precision, recall, F1-score, and AUC. Experimental results demonstrate that the proposed model achieves an accuracy of 94.2% and an AUC of 0.965, outperforming baseline CNN models by a significant margin. The use of Grad-CAM visualization provides interpretability by localizing tumor regions within MRI scans, thereby increasing the model’s clinical transparency. This study highlights the potential of explainable deep learning models to enhance diagnostic reliability in automated brain tumor detection systems.
PERAN ARTIFICIAL INTELLIGENCE DALAM MANAJEMEN INFORMASI KESEHATAN UNTUK OPTIMALISASI STRATEGI BISNIS DIGITAL M. Syahputra; Amalia Hanifa; Andri Swandi; Imrah Sari; Anita Citra Yeni
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol. 9 No. 2 (2026): April 2026
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/gj6yrg63

Abstract

The development of Artificial Intelligence (AI) has brought significant transformations in various sectors, including health information management. The integration of AI in health data management not only improves operational efficiency but also opens up strategic opportunities in digital business development in the healthcare sector. This study aims to analyze the role of AI in health information management and its impact on optimizing digital business strategies. The method used is a qualitative approach with a systematic literature review of various recent scientific publications (2020–2025) relevant to the research topic. Data were analyzed using thematic analysis techniques to identify patterns, trends, and contributions of AI in health information management. The results show that the application of AI, such as machine learning, natural language processing, and predictive analytics, can improve data processing accuracy, accelerate decision-making, and support personalized healthcare services. In addition, AI plays a role in increasing operational cost efficiency and creating innovative digital business models, such as telemedicine and intelligent-based electronic medical record systems. However, challenges related to data security, patient privacy, and infrastructure and human resource readiness remain major obstacles to its implementation. In conclusion, AI has a strategic role in improving the quality of health information management while strengthening the competitiveness of digital businesses in the healthcare sector. Therefore, adaptive policies, improved human resource competencies, and strengthened information security systems are needed to support the sustainable implementation of AI.
Co-Authors Amalia Hanifa Anita Citra Yeni Imrah Sari Irwandi Rizki Putra M. Syahputra Tasya Destria Putri yulia Zulrahmadi