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All Journal Buletin Ilmiah Sarjana Teknik Elektro
Mohammed Aquil Mirza
The Hong Kong Polytechnic University (PolyU)
Electrical & Electronics Engineering

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A Lightweight Hybrid Template-Matching–CNN Framework with Attention-Guided Fusion for Robust Small Object Detection Hewa Majeed Zangana; Marwan Omar; Mohammed Aquil Mirza; Xinwei Cao; Sharyar Wani
Buletin Ilmiah Sarjana Teknik Elektro Vol. 8 No. 1 (2026): February
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v8i1.14751

Abstract

Small object detection in aerial and surveillance imagery remains challenging due to low resolution, occlusion, and background clutter. This study introduces a novel hybrid detection framework that fuses template matching with a deep learning detector (Faster R-CNN) through an attention-guided decision fusion mechanism. The novelty lies in (i) a dual-stage fusion pipeline that integrates precise structural cues from template matching with deep semantic features, and (ii) a custom scale-aware focal loss, adapted from Focal Loss to emphasize hard and small objects by dynamically increasing penalties for low-confidence predictions. Evaluated on a Pascal VOC subset (1000 images, 5 classes), the proposed system achieves an mAP improvement of 3.5% over the Faster R-CNN baseline and surpasses YOLO-Lite and R-CNN variants in precision and recall. The hybrid design adds only a minimal computational overhead (0.45 s/image vs. 0.42 s for Faster R-CNN), demonstrating favorable efficiency–accuracy trade-offs suitable for scalable deployment. These findings highlight the framework’s robustness, particularly in scenes containing occlusion, clutter, or visually small targets. Limitations regarding template dependency are discussed, along with future directions for automatic template generation and real-time video adaptation.
Hybrid Vision Transformer for Brain and Lung Tumor Detection: A Multi-Modal Approach Using MRI (BraTS) and CT (LUNA16) Datasets Hewa Majeed Zangana; Mohammed Aquil Mirza; Sharyar Wani; Xinwei Cao; Marwan Omar
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 4 (2025): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i4.14766

Abstract

The integration of artificial intelligence (AI) into medical imaging has transformed clinical diagnostics, yet existing CNN-based systems still struggle with capturing global spatial context and generalizing across modalities. This study addresses this gap by proposing a hybrid Vision Transformer (ViT) architecture for tumor detection in MRI and CT scans, evaluated on two benchmark datasets: BraTS (brain MRI) and LUNA16 (lung CT). The research contribution is a unified, end-to-end transformer model that processes heterogeneous modalities without traditional feature-level fusion. The proposed method incorporates convolutional layers for local feature extraction alongside transformer blocks for long-range dependency modeling. Extensive experiments demonstrate that our model achieves a 2.5% higher Dice score and 3.1% higher F1-score compared to state-of-the-art CNN-based baselines, with accuracy reaching 95.4% on BraTS and 93.6% on LUNA16. Attention-based heatmaps further enhance explainability by highlighting clinically relevant tumor regions. These results show that hybrid transformers offer a robust and interpretable framework for multi-modal tumor detection, paving the way for more reliable and transparent AI-assisted radiological diagnostics.
Co-Authors Hewa Majeed Zangana Marwan Omar Sharyar Wani Xinwei Cao