Hierarchical Multiclass Classification Of Non-Small Cell Lung Cancer Leveraging Efficientnet-B3 For Enhanced Diagostic Precision
Keywords:
NSCLC classification, Detection, EfficientNet-B3, Transfer Learning, ImageNetAbstract
Among all forms of cancer, lung cancer has the highest global prevalence and continues to be the leading cause of cancer-related mortality. Lung cancer is categorized into two distinct forms: non-small cell lung cancer (NSCLC), which constitutes approximately 85% of cases, and small cell lung cancer (SCLC), a less common but more aggressive subtype. The primary subtypes of NSCLC include large cell carcinoma, squamous carcinoma, and adenocarcinoma which differ in histopathological features, molecular markers, and treatment responses. CT scan imaging plays a crucial role in accurately diagnosing lung cancer, facilitating the generation of high-quality diagnostic images for further analysis. Deep learning-based convolutional neural networks (CNNs) have proven highly effective in detecting and classifying lung cancer, leveraging hierarchical feature extraction from CT scan images. While CNNs demonstrate strong performance in lung cancer detection, they suffer from high computational costs and prolonged training times. Transfer learning addresses these limitations by leveraging pre-trained models, making it particularly effective for medical imaging with scare-labelled data. In this paper, we investigate the use of EfficientNet-B3 to enhance the accuracy of lung cancer detection. The model’s performance is evaluated using various metrics, including accuracy, precision, recall, F1-score, and ROC curve. Comparative analysis shows that ResNet achieves 91% accuracy, while VGG-16 attains 94%. Further improvements are observed with EfficientNet-B3, which achieves an accuracy of 98% making it the most accurate model among the assessed
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