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ESTD Year: 2017 | Impact Factor: 6.9
DOI Prefix: 10.47001/IRJIET
ESTD Year: 2017 | Impact Factor: 6.9
DOI Prefix: 10.47001/IRJIET
Vol 10 No 8 (2026): Volume 10, Issue 8, August 2026 | Pages: 123-128
International Research Journal of Innovations in Engineering and Technology
OPEN ACCESS | Research Article | Published Date: 31-08-2026
Early detection of lung nodules from computed tomography images is important for supporting timely lung cancer diagnosis. The research work represents a novel Pre-activation residual convolutional neural network with long short-term memory for lung nodule detection using LIDC dataset respectively. The proposed approach combines pre-activation residual learning with convolutional feature extraction to improve information propagation and preserve important image characteristics across deeper network layers. An LSTM component is incorporated to learn dependencies among the extracted feature representations and provide additional contextual information for classification. The framework includes image pre-processing, normalization, augmentation, feature extraction, sequential feature learning, and nodule classification consistently. The proposed approach is designed to capture both detailed local characteristics and higher-level contextual patterns associated with pulmonary nodules respectively. The performance of the classification and prediction accuracy were measured the area under receiver operating characteristic curve. The proposed method aims to provide a reliable and efficient computer-aided approach for automated lung nodule detection and to improve the consistency of computed tomography image analysis respectively.
Lung nodule detection, pre-activation residual CNN, LSTM, deep learning, computed tomography, LIDC dataset, lung cancer, medical image analysis, computer-aided detection
G. Paul Suthan, & Dr. Aruchamy Rajini. (2026). A Novel Enhanced Pre-Activation Residual CNN Approach for Lung Nodule Detection Using LIDC Dataset. International Research Journal of Innovations in Engineering and Technology - IRJIET, 10(8), 123-128. Article DOI https://doi.org/10.47001/IRJIET/2026.108013
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