Izvorni znanstveni članak
https://doi.org/10.1080/00051144.2024.2376776
An attention-based neural network for lung cancer classification and gradient in MRI
Poornima Ramasamy
; Department of Electronics and Communication Engineering, K.S.R. College of Engineering, Namakkal, Tamilnadu, India
*
Eatedal Alabdulkreem
; Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia
Nuha Alruwais
; Department of Computer Science and Engineering, College of Applied Studies and Community Services, King Saud University, Saudi Arabia, Riyadh, Saudi Arabia
V. P. Gladis Pushparathi
; Department of Computer Science and Engineering, Velammal Institute of Technology, Chennai, Tamilnadu
* Dopisni autor.
Sažetak
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.
Ključne riječi
Lung cancer; deep neural network; attention-based method; VGG19; magnetic resonance imaging
Hrčak ID:
326333
URI
Datum izdavanja:
21.7.2024.
Posjeta: 0 *