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Abstract

Quantum computing holds considerable promise for artificial intelligence (AI) in clinical decision support systems (CDSS), particularly in resource-constrained environments. This paper investigates a multiqubit quantum convolutional neural network (MQ-CNN) for medical diagnostics, leveraging param eterized quantum circuits to process low-resource healthcare datasets. We evaluate the framework on three binary classifica tion tasks: breast cancer (Wisconsin dataset), diabetes (Pima In dians), and heart failure prediction, using angle-encoded clinical features. The MQ-CNN achieves test accuracies of 82.4%, 98.7%, and 97.3% respectively, matching classical CNNs while reducing trainable parameters significantly. Comparative analysis shows the quantum model converges 22% faster than hybrid quantum classical counterparts under identical training conditions. Ro bustness evaluations confirm ≤3% accuracy degradation when subjected to 15% synthetic label noise. These results highlight the architecture’s suitability for resource-constrained environ ments, demonstrating that quantum-enhanced feature extraction can maintain diagnostic accuracy while significantly reducing computational overhead. This work provides empirical evidence for near-term quantum machine learning in practical healthcare applications.

BibTeX

@INPROCEEDINGS{11172247,
  author={Ovi, Tareque Bashar and Bashree, Nomaiya and Alam, Ayat Subah and Tanzim, Rawnak and Wahed, Md Abdul and Nyeem, Hussain},
  booktitle={2025 International Conference on Quantum Photonics, Artificial Intelligence, and Networking (QPAIN)}, 
  title={Multiqubit Quantum Convolutional Neural Networks for Efficient AI-Driven Healthcare Analytics}, 
  year={2025},
  volume={},
  number={},
  pages={1-6},
  keywords={Degradation;Decision support systems;Accuracy;Computational modeling;Qubit;Noise;Neural networks;Computer architecture;Feature extraction;Convolutional neural networks;Deep learning;QML;QCNN;feature interaction;qubit;health AI},
  doi={10.1109/QPAIN66474.2025.11172247}}

Abstract

Cross-domain variations in satellite imagery-ranging from sensor characteristics to urban morphology-undermine the reliability of deep segmentation models. We isolate the architectural factor of residual connections by comparing nine encoder-decoder designs, trained from scratch on the large-scale WHU and the compact Massachusetts building datasets without pretrained backbones or patch augmentation. Residual architectures (ResUNet, ResUNet++) consistently lead by margin: up to 1.6 % higher Dice and 2.5% higher IoU on WHU dataset, and a 5% Dice gain on Massachusetts dataset. Error-mode analysis shows that skip-augmented identity mappings preserve feature gradients across depth, reducing false negatives by as much as 12 % versus vanilla U-Net under data scarcity. These results demonstrate that residual links are a simple yet powerful strategy to fortify building segmentation against domain shifts in real-world satellite imagery.

BibTeX

S. B. Zahid, R. Tanzim, T. B. Ovi, N. Bashree, H. Nyeem and M. A. Wahed, "Impact of Residual Connections on Cross-Domain Generalization for Building Segmentation," 2025 International Conference on Quantum Photonics, Artificial Intelligence, and Networking (QPAIN), Rangpur, Bangladesh, 2025, pp. 1-6, doi: 10.1109/QPAIN66474.2025.11172159. keywords: {Training;Attention mechanisms;Architecture;Semantic segmentation;Buildings;Transfer learning;Transformers;Feature extraction;Satellite images;Remote sensing;building extraction;remote sensing;semantic segmentation;aerial imagery;attention mechanism;transformer;transfer learning},

Abstract

One of the most important machine vision tasks is monocular depth prediction, essential for applications such as autonomous navigation and augmented reality. While CNN-based architectures are widely used, they are limited by restricted receptive fields, which hinder effective multi-scale feature extraction. In order to overcome these issues, we develop Depth-PVT, an innovative architecture that integrates Efficient Channel Attention (ECA) with the Pyramid Vision Transformer (PVT). This integration enhances feature representation by emphasizing salient channel-wise features and suppressing irrelevant ones with minimal computational overhead. Depth-PVT incorporates three key modules within the PVT framework: the cascaded fusion module (CFM) for aggregating semantic and spatial information of high level features, the camouflage identification module (CIM) for capturing complex depth cues and low level features, and the similarity aggregation module (SAM) for fusing cross-scale features, thereby enriching depth d

BibTeX

@inproceedings{ovi2025depth,
  title={Depth-PVT: Pyramid Vision Transformer with Channel Attention for Depth Estimation},
  author={Ovi, Tareque Bashar and Bashree, Nomaiya and Tanzim, Rawnak and Tirtha, Anindya Chanda and Nyeem, Hussain and Wahed, Md Abdul},
  booktitle={2025 International Conference on Electrical, Computer and Communication Engineering (ECCE)},
  pages={1--6},
  year={2025},
  organization={IEEE}
}

Abstract

Plant diseases pose a significant threat to global food secu rity, demanding faster and more accessible methods for identification. While deep learning has shown promise in automating plant disease diagnosis from digital images, large model sizes remain a hurdle. This paper proposes a novel lightweight deep learning framework based on CovXNet, a convolutional neural network architecture utilizing efficient depthwise convolutions, combined with Squeeze-and-Excitation (SE) and Convolutional Block Attention Module (CBAM) attention mechanisms for enhanced performance. Our proposed models have been evaluated on the publicly available Plant Village dataset, achieving state-of-the art performance with 99.37% test accuracy using CovXNet with SE and 99.30% using CovXNet with CBAM across 38 classes. These re sults demonstrate the effectiveness of our approach in facilitating accu rate and efficient plant disease diagnosis, particularly for deployment on resource-constrained devices.

BibTeX

@inproceedings{chowdhury2024attention,
  title={Attention-Enhanced Multi-dilation CNN for Plant Disease Classification},
  author={Chowdhury, Disha and Bashree, Nomaiya and Ovi, Tareque Bashar and Nyeem, Hussain and Wahed, Md Abdul and Tanzim, Rawnak},
  booktitle={International Conference on Human-Centric Smart Computing},
  pages={111--121},
  year={2024},
  organization={Springer}
}