Article: An efficient lung nodule detection model from 3D CT images with residual bidirectional long short-term memory and adaptive segmentation schemes Journal: International Journal of Ad Hoc and Ubiquitous Computing (IJAHUC) 2025 Vol.50 No.2 pp.73 - 90 Abstract: Presently, numerous advancements are achieved in biomedical imaging techniques to provide huge openings in the healthcare industries. Therefore, an effective lung nodule identification mechanism by a deep learning technique is implemented in this paper. Initially, the three-dimensional (3D) computed tomography (CT) images are acquired from standard database. Then, the gathered 3D CT images are offered as input to the segmentation region in which the adaptive 3D Trans-DenseUNet (A-3D-TransDUNet) model is employed and the parameters are tuned with fitness-based cicada swarm optimisation (FCSO) algorithm. Then, the acquired segmented lung nodule images are given to the deep learning-based detection framework. Here, the multiscale 3D DenseNet fused with 'residual bidirectional long short-term memory (M3D-DNet-RBi-LSTM)' is used as the detection framework. The final detected lung nodules are obtained from the implemented M3D-DNet-RBi-LSTM model. Various experimentations are executed to validate the efficacy rate provided by the suggested deep learning-aided lung nodule detection framework. Inderscience Publishers - linking academia, business and industry through research

Title: An efficient lung nodule detection model from 3D CT images with residual bidirectional long short-term memory and adaptive segmentation schemes

Authors: S. Maheswari; S. Suresh

Addresses: Department of Computer Science and Engineering, SRM Institute of Science and Technology, Ramapuram, Chennai, Tamil Nadu 600089, India ' Department of Computer Science and Engineering, SRM Institute of Science and Technology, Ramapuram, Chennai, Tamil Nadu 600089, India

Abstract: Presently, numerous advancements are achieved in biomedical imaging techniques to provide huge openings in the healthcare industries. Therefore, an effective lung nodule identification mechanism by a deep learning technique is implemented in this paper. Initially, the three-dimensional (3D) computed tomography (CT) images are acquired from standard database. Then, the gathered 3D CT images are offered as input to the segmentation region in which the adaptive 3D Trans-DenseUNet (A-3D-TransDUNet) model is employed and the parameters are tuned with fitness-based cicada swarm optimisation (FCSO) algorithm. Then, the acquired segmented lung nodule images are given to the deep learning-based detection framework. Here, the multiscale 3D DenseNet fused with 'residual bidirectional long short-term memory (M3D-DNet-RBi-LSTM)' is used as the detection framework. The final detected lung nodules are obtained from the implemented M3D-DNet-RBi-LSTM model. Various experimentations are executed to validate the efficacy rate provided by the suggested deep learning-aided lung nodule detection framework.

Keywords: lung nodule detection; adaptive 3-dimensional Trans-DenseUNet; multiscale 3D DenseNet fused with residual bidirectional long short-term memory; fitness-based cicada swarm optimisation; FCSO.

DOI: 10.1504/IJAHUC.2025.149464

International Journal of Ad Hoc and Ubiquitous Computing, 2025 Vol.50 No.2, pp.73 - 90

Received: 22 Feb 2024
Accepted: 25 Feb 2025
Published online: 01 Nov 2025 *

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Title: An efficient lung nodule detection model from 3D CT images with residual bidirectional long short-term memory and adaptive segmentation schemes

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