Enhancing Histopathological Lung Cancer Diagnosis Through Explainable Deep Learning Models: A Methodological Framework Proposal

The growing adoption of deep learning (DL) in early-stage cancer diagnosis has demonstrated remarkable performance across multiple imaging tasks. Yet, the lack of transparency in these models (“black-box” problem) limits their adoption in clinical environments. This study proposes a methodological framework for developing interpretable DL models to support the early histopathological diagnosis of lung cancer, with a focus on adenocarcinoma and squamous cell carcinoma. The approach leverages publicly available datasets (TCGA-LUAD, TCGA-LUSC, LC25000) and employs high-performing architectures such as EfficientNet, along with post hoc explainability techniques including Grad-CAM and SHAP. Data will be pre-processed and sampled using stratified purposeful strategies to ensure diversity and balance across subtypes and stages. Model evaluation will combine standard performance metrics with clinician feedback and the spatial alignment of visual explanations with ground-truth annotations. While implementation remains a future step, this paper proposes a methodological framework designed to guide the development of DL systems that are not only accurate but also interpretable and clinically meaningful.