Enhanced Lung Tumor Detection via Deep Learning Techniques in CT Imaging

Lung cancer is one of the most common causes of cancer-related death worldwide. Early detection is essential for better patient outcomes. Both the YOLOv8 algorithm and Convolutional Neural Networks (CNNs) have demonstrated promise in the field of medical image processing and object detection, respectively. In this research, we present an innovative method for the detection of lung cancer utilising a CNN model and the YOLOv8 algorithm, integrated into a mobile application developed in Kotlin for enhanced accessibility. Our system takes advantage of the YOLOv8 algorithm's real-time object detection capabilities to recognise nodules of lung cancer using CT scans. The CNN model is trained on a dataset of CT scans and is capable of differentiating between benign and malignant nodules. The mobile application provides a user-friendly interface for uploading CT scans and receiving real-time diagnostic results. The proposed system intends to improve lung cancer early detection by providing a convenient and efficient tool for healthcare professionals and patients. By utilising the strength of CNNs and YOLOv8, this technology has the capacity to reduce false negatives and improve overall diagnostic accuracy, ultimately contributing to better patient care and outcomes. Our model performs well overall, with 98.06% precision, 96.57% recall and 97.31% F1 score when test on a benchmark dataset of images of lung cancer.