Convolutional Block Attention Module

Also known as CBAM

Latest papers

8 papers1 paper in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Energy Efficient Lung Tumor Segmentation with a modified CBAM ENet Skip Model
Oct 24, 2026Sharan Thummagunti, Aayushi Padia, Abhiram Koppuravuri +2Convolutional Block Attention Module Medical Image Segmentation
Department of Data Science and Artificial Intelligence, Indian Institute of Information Technology Dharwad, Ittigatti Road, Dharwad, 580009, Karnataka, India. · Department of Computer Science and Engineering, Indian Institute of Information Technology Dharwad, Ittigatti Road, Dharwad, 580009, Karnataka, India.
TEEP-RCNN: Texture-Enhanced Edge-aware Perception for Steel Surface Defect Detection via Improved Convolutional Block Attention in Faster R-CNN
Sep 23, 2026Kirtan RajeshObject Detection Convolutional Neural Networks
Independent Researcher
Attention-Enhanced Deep Features with Heterogeneous Ensemble Learning for Glaucoma Detection
Sep 6, 2026Abdullah Al Shafi, Nishat Sadaf Lira, Abrar Hasan +2Convolutional Block Attention Module Glaucoma Detection
Department of CSE, Khulna University of Engineering & Technology, Bangladesh · Department of CSE, Daffodil International University, Bangladesh · Department of SWE, Green University of Bangladesh
Attention-Guided Efficientnet Architecture For Precise Criminal Identification in Surveillance Images
Jul 3, 2026Savitha N J, Lata B TFace Recognition Convolutional Block Attention Module
Research Scholar, Department of Computer Science and Engineering, University Visvesvaraya College of Engineering (UVCE), Bangalore University, CMR Institute of Technology, Bengaluru, India · Department of Computer Science and Engineering, University Visvesvaraya College of Engineering (UVCE), Bangalore University, Bengaluru, India
Uncertainty-Aware Wildfire Smoke Density Classification from Satellite Imagery via CBAM-Augmented EfficientNet with Evidential Deep Learning
May 15, 2026Ranjith ChodavarapuRemote Sensing Image Understanding Evidential Deep Learning
Kent State University
Improving Imbalanced Multi-Label Chest X-Ray Diagnosis via CBAM-Enhanced CNN Backbones
May 4, 2026Duy Nguyen Huu, Duy Hoang Khuong, Ngu Huynh Cong VietClass-Imbalanced Learning Chest X-Ray Classification
Department of Artificial Intelligence FPT University Ho Chi Minh City, Vietnam · Department of Computing Fundamental FPT University Ho Chi Minh City, Vietnam
CBAM-Enhanced DenseNet121 for Multi-Class Chest X-Ray Classification with Grad-CAM Explainability
Apr 14, 2026Utsho Kumar DeyConvolutional Block Attention Module Chest X-Ray Classification
Department of Computer Science and Engineering, Northern University of Business and Technology Khulna, Bangladesh
A Novel Approach to Chest X-ray Lung Segmentation Using U-net and Modified Convolutional Block Attention Module
Apr 22, 2024Mohammad Ali Labbaf Khaniki, Marzieh Mirzaeibonehkhater, Mohammad Manthouri +1Convolutional Block Attention Module Medical Image Segmentation
Faculty of Electrical Engineering, K.N. Toosi University of Technology, Tehran, Iran

Oct 24, 2026J/K move · Enter open · S save

Energy Efficient Lung Tumor Segmentation with a modified CBAM ENet Skip Model

Sharan Thummagunti, Aayushi Padia, Abhiram Koppuravuri, Prabhu Prasad B M+1

Department of Data Science and Artificial Intelligence, Indian Institute of Information Technology Dharwad, Ittigatti Road, Dharwad, 580009, Karnataka, India. · Department of Computer Science and Engineering, Indian Institute of Information Technology Dharwad, Ittigatti Road, Dharwad, 580009, Karnataka, India.

Convolutional Block Attention Module Medical Image Segmentation

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Lung tumor segmentation plays a vital role in early detection and treatment planning of lung cancer. Although state-of-the-art deep learning models achieve high dice score coefficients (DSC), each forward pass requires computing large amounts of floating point operations, making them computationally expensive, requiring GPU acceleration, and having slow inference speed, limiting real-world clinical deployment. We propose a lightweight, energy-efficient model that leverages the ENet architecture for its real-time inference capability and low computational cost, enhanced with UNet-style skip connections between encoder and decoder stages for feature reuse and the lightweight Convolutional Block Attention Module (CBAM) for improved feature representation. The model is trained on publicly available data from The Cancer Imaging Archive (TCIA) via the Medical Segmentation Decathlon Challenge (MSD) and evaluated on the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) dataset. Our model achieves a DSC of 0.90812 (threshold of 0.5) with its compact architecture of 430,052 parameters and 0.549 Giga Multiply-Accumulate operations (GMAC), making it up to 10 times faster, requires 79 times less GMAC than UNet, and has 22 times fewer parameters than SegNet. These characteristics make it well-suited for deployment in clinical settings where computational resources are limited and decrease the cost of utilizing such a system. Additionally, the explainable AI technique of Grad-CAM is used to provide visual interpretability of the model's predictions and to identify regions where segmentation performance requires improvement.