Advanced Lung Nodule Segmentation and Classification for Early Detection of Lung Cancer using SAM and Transfer Learning

TL;DR

This paper presents a novel lung nodule segmentation and classification method using the Segment Anything Model (SAM) combined with transfer learning, significantly improving early lung cancer detection accuracy in medical imaging.

Contribution

It introduces an innovative approach that leverages SAM and transfer learning for precise lung nodule segmentation and classification, enhancing CAD system performance.

Findings

Achieved a Dice Similarity Coefficient of 97.08%

Attained an Intersection over Union of 95.6%

Classification accuracy of 96.71%

Abstract

Lung cancer is an extremely lethal disease primarily due to its late-stage diagnosis and significant mortality rate, making it the major cause of cancer-related demises globally. Machine Learning (ML) and Convolution Neural network (CNN) based Deep Learning (DL) techniques are primarily used for precise segmentation and classification of cancerous nodules in the CT (Computed Tomography) or MRI images. This study introduces an innovative approach to lung nodule segmentation by utilizing the Segment Anything Model (SAM) combined with transfer learning techniques. Precise segmentation of lung nodules is crucial for the early detection of lung cancer. The proposed method leverages Bounding Box prompts and a vision transformer model to enhance segmentation performance, achieving high accuracy, Dice Similarity Coefficient (DSC) and Intersection over Union (IoU) metrics. The integration of SAM and Transfer Learning significantly improves Computer-Aided Detection (CAD) systems in medical imaging, particularly for lung cancer diagnosis. The findings demonstrate the proposed model effectiveness in precisely segmenting lung nodules from CT scans, underscoring its potential to advance early detection and improve patient care outcomes in lung cancer diagnosis. The results show SAM Model with transfer learning achieving a DSC of 97.08% and an IoU of 95.6%, for segmentation and accuracy of 96.71% for classification indicates that ,its performance is noteworthy compared to existing techniques.