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International Journal of Advances in Intelligent Informatics
Published by Universitas Ahmad Dahlan
ISSN : 24426571     EISSN : 25483161     DOI : 10.26555
Core Subject : Science,
Computer Science & IT
International journal of advances in intelligent informatics (IJAIN) e-ISSN: 2442-6571 is a peer reviewed open-access journal published three times a year in English-language, provides scientists and engineers throughout the world for the exchange and dissemination of theoretical and practice-oriented papers dealing with advances in intelligent informatics. All the papers are refereed by two international reviewers, accepted papers will be available on line (free access), and no publication fee for authors.
Arjuna Subject : -
Articles 353 Documents
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Surgical-aware video masked autoencoders with phase-conditioned attention for laparoscopic action recognition Hakim Nasaoui; Hassan Silkan; Insaf Bellamine
International Journal of Advances in Intelligent Informatics Vol 12, No 2 (2026): May 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Fine-grained surgical action recognition in laparoscopic videos remains a challenge even with recent deep learning progress. While current VideoMAE approaches reach 89.11% accuracy on cholecystectomy tasks, they face specific limitations. Random masking strategies often miss surgical instruments that occupy only 10% to 15% of frames. Furthermore, context-independent models struggle with visually similar actions across different phases, and symmetric two-stream architectures tend to waste computational resources. To solve this, we developed SA-VideoMAE, a surgical-aware video masked autoencoder specifically designed for laparoscopic action recognition. Our method utilizes surgical-aware adaptive masking that integrates YOLOv7x object detection to prioritize instrument patches. This increased instrument visibility from 10% to 60% during training, ensuring the model focuses on action-relevant regions rather than static backgrounds. We also utilized phase-conditioned hierarchical attention to inject learnable phase embeddings into the attention mechanisms, enabling the model to disambiguate visually similar actions based on surgical context. For efficiency, our asymmetric dual-stream architecture processes RGB with ViT-Base (86M parameters) and optical flow with ViT-Tiny (5.7M parameters), which achieved a 47% parameter reduction compared to symmetric designs. Our training process then balanced reconstruction, classification, temporal consistency, and phase prediction through a novel multi-objective optimization strategy. Results on Cholec80's Calot's Triangle Dissection phase show 93.5% accuracy, representing a 4.4 percentage point improvement over the verified baseline. Notably, challenging action recall improved from 51% to 74% while maintaining real-time inference at 62ms per clip. These findings demonstrate that encoding surgical domain knowledge into video architectures significantly enhances action recognition performance.
Enhancing clinical safety in automated breast ultrasound segmentation through a sensitivity-prioritized detection and statistical fail-safe mechanism Daffa Muhamad Azhar; Nanik Suciati
International Journal of Advances in Intelligent Informatics Vol 12, No 2 (2026): May 2026
Publisher : Universitas Ahmad Dahlan

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Abstract

Accurate breast ultrasound (BUS) lesion segmentation is critical for early diagnosis but is challenged by image artifacts and the reliance of foundation models on manual prompting. Existing automated frameworks often lack robust fail-safe mechanisms, leading to missed diagnoses. To address this reliability gap, this study proposes a novel, fully automated hybrid segmentation framework that synergistically integrates three key components: (1) a recall-optimized YOLOv9 detector tailored to minimize clinical false negatives; (2) a MedSAM2 foundation model efficiently fine-tuned via Low-Rank Adaptation (LoRA) for ultrasound specifics; and (3) a statistical fallback mechanism that acts as a crucial safety net to recover spatial prompts during detection failures. Evaluated on the public BUSI dataset, the recall-dominant detection module achieved a Recall of 0.8238. Supported by this robust prompting and fallback strategy, the segmentation module achieved a Dice coefficient of 0.8818 and an IoU of 0.8113. By effectively integrating specialized detection with adaptive segmentation and a statistical fail-safe, the proposed pipeline offers a highly reliable automated approach for computer-aided screening systems.
Enhancing chest image classification using PCA and CNN model Thair A. Kadhim
International Journal of Advances in Intelligent Informatics Vol 12, No 2 (2026): May 2026
Publisher : Universitas Ahmad Dahlan

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Abstract

Deep learning (DL) has a significant impact on X-ray images for diagnosing and categorizing a range of lung disorders. The proliferation of extensive annotated image datasets has resulted in the emergence of convolutional neural networks (CNNs) as a useful instrument for the tasks of image recognition and categorization. Despite this abundance, the primary challenge in medical diagnosis continues to be these images' classification. This research is to enhance image classifiers by using the CNN model; both training and testing datasets underwent analysis using the suggested CNN system. An analysis and comparison are conducted on the impact of feature extraction using the Principal Components Algorithm (PCA) technique. The study attains maximal classification efficiency by preparing images by dimensionality reduction before classification, concurrently enhancing the efficacy of CNNs in feature extraction. The optimal tuning strategies for enhancing the performance of the proposed CNN were found to include boosting the quantity of epochs, changing the optimizer, and decreasing the learning rate, as well as improving algorithm gains by using pre-trained weights. The suggested system outperforms previously utilized approaches like VGG or DenseNet, with more than 99.80 percent accuracy, precision, recall, and F1-score values. The suggested methodology demonstrates considerable promise for enhancing the efficacy and precision of lung disease diagnoses derived from chest X-ray images, thereby offering clinicians beneficial decision support and accelerating the implementation of treatment strategies. Furthermore, the developed model facilitates the identification of pulmonary ailments, encompassing critical conditions like COVID-19, thereby facilitating timely and efficacious patient care.

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