Lung Nodule Detection For CT-Guided Biopsy Images Using Deep Learning

Abstract

The recent advancements in artificial intelligence enhance the detection and classification of lung nodules via computed tomography scans, addressing the critical need for early diagnosis of lung cancer. The lung cancer when identified at the earlier stages, the chance of survival is higher. The methodology encompasses a modern deep-learning approach applied to a private dataset obtained from the Barnard Institute of Radiology at Madras Medical College, Chennai, which has been granted ethical approval. The results from applying the proposed Convolutional Neural Network model are promising, with an accuracy of 99.3% in malignancy detection, signifying a notable advancement in the precise diagnosis of lung cancer through non-invasive imaging techniques. Beyond academia, the findings of this study have significant implications for real-world healthcare settings. By providing a reliable and automated solution for lung nodule detection, this research contributes to early diagnosis and personalized treatment strategies for lung cancer patients. The value of the present work lies in its potential to reduce morbidity through the early detection of lung cancer, thus contributing to both clinical practice and the ongoing development of AI applications in healthcare. Our research may serve as a model for further studies in digital health care at Madras Medical College, aiming to improve patient outcomes through technology-driven diagnostics.

Keywords

• Convolutional Neural Network
• Deep Learning Technique
• Lung Tumor Stages
• Benign and Malignancy Lesions Specification
• Lung Cancer Predication