cs.CVOct 24, 2026

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

Authors: Sharan Thummagunti, Aayushi Padia, Abhiram Koppuravuri, Prabhu Prasad B M, G N Girish

Organizations: 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.

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Abstract

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.

Figures & tables

Figure 1: 2D slices from the MSD dataset with the CT slice (left), the ground truth segmentation mask (middle) and the overlap of both (right).
Figure 2: 2D slices from the LIDC-IDRI dataset with poor illumination conditions.
Figure 3: (a) Initial block (b) Regular bottleneck module (c) Downsampling bottleneck module (d) Upsampling bottleneck module
NameTypeIntegrationOutput sizeStage
initial  16×128×12816\times 128\times 128Initial
bottleneck1.0downsampling 64×64×6464\times 64\times 64Stage 1
bottleneck1.1  64×64×6464\times 64\times 64
bottleneck1.2  64×64×6464\times 64\times 64
bottleneck1.3  64×64×6464\times 64\times 64
bottleneck1.4  64×64×6464\times 64\times 64
Table 1: Model architecture for an input image resolution of 1×256×2561\times 256\times 256 .
HyperparameterValue
Image dimensions256×256256\times 256
Batch size16
No. of epochs250
OptimizerAdam
Learning rate1×10−41\times 10^{-4}
Output activation headSigmoid
Table 2: Selected hyperparameter values for model training.
Figure 4: A sample of 9 augmented images of a CT slice after passing through the data augmentation pipeline.
ModelDSCPrecisionRecallIoU
UNet Ronneberger et al. (2015)0.9058260.9261470.8863770.827863
SegNet Badrinarayanan et al. (2017)0.9005290.9125120.8888570.819057
ENet Paszke et al. (2016)0.8431470.8803150.8089910.728829
ENet + Skip0.8457990.8711460.8218850.732800
Proposed model0.908120.9174520.8989760.831703
Table 3: Evaluation metric scores comparison over different architectures on the LIDC-IDRI dataset with threshold of 0.5. 0 0 footnotetext: These model have been evaluated through experimentation.
Figure 5: Precision-Recall Curves with threshold overlay for different architectures.
ModelDSC
UNet with MobileNetV2 encoder Riaz et al. (2023)0.8793
UNet with MobileNetV3 encoder Suji et al. (2024) 1 1 1 These model have been evaluated through experimentation0.8824
UNet with EfficientNetB0 encoder Suji et al. (2024) 1 1 1 These model have been evaluated through experimentation0.8503
nnU-Net Ferrante et al. (2022)0.9
3D-Res2UNet Xiao et al. (2020)0.9530
RRc-Unet 3D Le and Saut (2023)0.8777
Table 4: Dice Score Coefficient (DSC) comparison with existing architectures.
Figure 6: Tumor slice with the ground truth mask overlapped (left), heatmap overlay of Grad-CAM (middle) and model segmentation mask prediction (right).
ModelNo. of parametersGMACinference time (sec)
UNet Ronneberger et al. (2015)7,781,76142.35168.05
SegNet Badrinarayanan et al. (2017)9,374,59326.6893.94
UNet with MobileNetV2 encoder Riaz et al. (2023)12,366,9778.1227.82
UNet with MobileNetV3 encoder Suji et al. (2024)9,370,5617.1722.59
UNet with EfficientNetB0 encoder Suji et al. (2024)14,233,5978.1231.29
3D-Res2UNet Xiao et al. (2020)6,767,889229.66623.18
Table 5: Number of parameters, computational efficiency and inference time on CPU to process a CT volume of dimensions 256×256×384256\times 256\times 384 .
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