An attention-based neural network for lung cancer classification and gradient in MRI

Autor: Poornima Ramasamy, Eatedal Alabdulkreem, Nuha Alruwais, V. P. Gladis Pushparathi
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Automatika, Vol 65, Iss 4, Pp 1379-1390 (2024)
Druh dokumentu: article
ISSN: 00051144
1848-3380
0005-1144
DOI: 10.1080/00051144.2024.2376776
Popis: Accurate lung cancer classification in magnetic resonance imaging (MRI) remains challenging due to the difficulty in detecting cancerous patterns. In response, this study introduces an attention-based VGG19 neural network for enhanced classification performance. Leveraging the VGG19 architecture's deep learning capabilities, our model incorporates attention mechanisms to selectively emphasize salient features during training. The attention-based approach addresses the challenge of discerning subtle patterns indicative of malignancy, significantly improving classification accuracy. We train and evaluate the model on a diverse dataset, ensuring its capacity to generalize across various patient cases. The attention mechanism proves effective in prioritizing critical regions within MRI scans, enhancing sensitivity and specificity in lung cancer detection. Additionally, we employ gradient analysis to interpret the decision-making process, providing valuable insights into influential features. Results demonstrate the proposed model's superiority over baseline approaches, showcasing its efficacy in inaccurate lung cancer classification. The attention-based VGG19 neural network not only advances classification capabilities but also offers interpretability crucial for gaining trust in automated diagnostic systems. This research contributes a robust solution to a pressing medical imaging challenge, holding promise for practical implementation in clinical settings to support radiologists in timely and accurate lung cancer diagnosis.
Databáze: Directory of Open Access Journals
Externí odkaz: