Self-attention driven transfer learnt deep neural network for improved lung cancer detection from radiographic images

<h2>Abstract</h2> This article proposes a novel deep neural network-based method to enhance image classification, focusing on lung cancer detection. Integrating a self-attention mechanism into a pre-trained VGG16 network, the approach combines the robustness of pre-trained convolutional neural networks (CNNs) with the interpretability of self-attention. By using the transformer model's scaled dot-product attention mechanism, attention scores are calculated to effectively focus on critical areas within input images. This increased attention and global relationship enable more precise extraction and representation of features, and addresses nuanced patterns in lung cancer images. Experimental results on the IQ-OTH/NCCD lung cancer dataset show that the proposed model demonstrates high performance in accurately classifying lung cancer, with average precision, recall, and F1-score of 98.25%, 97.96%, and 98.10%, respectively, and an average accuracy of 97.96% (best accuracy 98.64%). The proposed model has also demonstrated an average accuracy of 99.36% (best accuracy 99.54%) on 5-fold cross-validation and is seen to be outperforming 13 other state-of-the-art approaches on similar dataset with only 76,292 parameters. Statistical analysis, including an unpaired t-test and Welch's t-test, confirms the superiority of the proposed method over VGG16, with a two-tailed p value of 0.0319 and 0.0278, respectively. These findings assert the effectiveness of the lightweight model for real-world applications in medical image analysis. This technique allows to combine fine-grained detail extraction with global attention, enabling precise localization and accurate assessment, thus enhancing sensitivity to subtle variations crucial for medical diagnosis.