  Lung cancer poses a significant global public health challenge, emphasizing
the importance of early detection for improved patient outcomes. Recent
advancements in deep learning algorithms have shown promising results in
medical image analysis. This study aims to explore the application of object
detection particularly YOLOv5, an advanced object identification system, in
medical imaging for lung cancer identification. To train and evaluate the
algorithm, a dataset comprising chest X-rays and corresponding annotations was
obtained from Kaggle. The YOLOv5 model was employed to train an algorithm
capable of detecting cancerous lung lesions. The training process involved
optimizing hyperparameters and utilizing augmentation techniques to enhance the
model's performance. The trained YOLOv5 model exhibited exceptional proficiency
in identifying lung cancer lesions, displaying high accuracy and recall rates.
It successfully pinpointed malignant areas in chest radiographs, as validated
by a separate test set where it outperformed previous techniques. Additionally,
the YOLOv5 model demonstrated computational efficiency, enabling real-time
detection and making it suitable for integration into clinical procedures. This
proposed approach holds promise in assisting radiologists in the early
discovery and diagnosis of lung cancer, ultimately leading to prompt treatment
and improved patient outcomes.
