Abstract This study addresses the diagnostic challenges associated with Non-Small Cell Lung Cancer (NSCLC), the most prevalent form of lung cancer often diagnosed at advanced stages. It aims to develop a computer-aided classification model exclusively utilizing medical images from Positron Emission Tomography (PET) and Computed Tomography (CT) scans. The model identifies benign/malignant Solitary Pulmonary Nodules (SPN) related to NSCLC. A dataset comprising of 456 patients, in total, was curated, featuring 48.68 % benign cases. To achieve its objective, four well-established Deep Learning (DL) algorithms were employed. The dataset was split into three different groups of images, each used for a particular task; training, testing and validation of the model. Notably, the study extends beyond predictive accuracy by delving into the prediction process of the best-performing model, thereby enhancing the explainability of the typically opaque Artificial Intelligence (AI) models. This explainability aspect aims to foster trust and confidence in the model’s outcomes, allowing users to comprehend the decision-making process. The results indicate that the YOLOv8 algorithm emerged as the most accurate classification model, achieving a maximum accuracy of 91.3 % and a maximum True Positive Rate (TPR) of 93.62 %. This study’s importance lies in underscoring the potential of DL approaches in improving NSCLC diagnosis while providing a transparent and understandable classification mechanism. It offers a novel way of explaining classification results from YOLOv8 model and it demonstrates both the effectiveness of DL-assisted predictions in characterizing SPNs and the added value of interpretability, thereby offering a holistic perspective on the model’s capabilities.
Samaras et al. (Thu,) studied this question.