BanglaBirds-AttnNet: A Framework for Classification Endangered Bangladeshi Birds Using EfficientNetB0 with CBAM Enhanced By Explainable AI

DOI

10.2991/978-94-6239-664-7_53How to use a DOI?

Keywords

BangladeshiBirds; Endangered Species Classification; EfficientNetB0; Convolutional Block Attention Module (CBAM); Deep Learning; Explainable Artificial Intelligence (XAI)

Abstract

Birds are vital indicators of ecosystem health, yet numerous species in Bangladesh are threatened by habitat loss and environmental change. Existing bird classification models often employ black-box architectures lacking interpretability and dataset specificity. This paper introduces BanglaBirds-AttnNet, a deep learning framework that combines EfficientNetB0 with a Convolutional Block Attention Module (CBAM) to enhance spatial-channel feature learning for bird classification. Trained on the BanglaBirds dataset, which contains 18 native and endangered species, the proposed model achieved 99% classification accuracy, outperforming MobileNet, ViT, and DarkNet- based approaches. The inclusion of explainable AI improves the transparency and interpretability of predictions, enabling reliable real-world deployment for ecological monitoring. BanglaBirds-AttnNet thus represents a significant advancement in AI-driven biodiversity conservation, delivering both high accuracy and explainability for endangered bird classification in Bangladesh.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Md. Abu Raihan
AU  - Sadia Bristi
PY  - 2026
DA  - 2026/06/08
TI  - BanglaBirds-AttnNet: A Framework for Classification Endangered Bangladeshi Birds Using EfficientNetB0 with CBAM Enhanced By Explainable AI
BT  - Proceedings of the International Conference on Intelligent Data Analysis and Applications (IDAA 2025)
PB  - Atlantis Press
SP  - 772
EP  - 785
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-664-7_53
DO  - 10.2991/978-94-6239-664-7_53
ID  - Raihan2026
ER  -