  Lung cancer is highly lethal, emphasizing the critical need for early
detection. However, identifying lung nodules poses significant challenges for
radiologists, who rely heavily on their expertise for accurate diagnosis. To
address this issue, computer-aided diagnosis (CAD) systems based on machine
learning techniques have emerged to assist doctors in identifying lung nodules
from computed tomography (CT) scans. Unfortunately, existing networks in this
domain often suffer from computational complexity, leading to high rates of
false negatives and false positives, limiting their effectiveness. To address
these challenges, we present an innovative model that harnesses the strengths
of both convolutional neural networks and vision transformers. Inspired by
object detection in videos, we treat each 3D CT image as a video, individual
slices as frames, and lung nodules as objects, enabling a time-series
application. The primary objective of our work is to overcome hardware
limitations during model training, allowing for efficient processing of 2D data
while utilizing inter-slice information for accurate identification based on 3D
image context. We validated the proposed network by applying a 10-fold
cross-validation technique to the publicly available Lung Nodule Analysis 2016
dataset. Our proposed architecture achieves an average sensitivity criterion of
97.84% and a competition performance metrics (CPM) of 96.0% with few
parameters. Comparative analysis with state-of-the-art advancements in lung
nodule identification demonstrates the significant accuracy achieved by our
proposed model.
