Advancing Oncology Diagnostics: AI-Enabled Early Detection of Lung Cancer Through Hybrid Histological Image Analysis

Abstract

Against the backdrop of the pervasive global challenge of cancer, with particular emphasis on lung cancer (LC), this study centers its investigation on the critical realm of early detection leveraging artificial intelligence (AI) within the domain of histological image analysis. Through the fusion of DenseNet201 with color histogram techniques, a novel hybrid feature set emerges, engineered to elevate classification accuracy. The comprehensive evaluation encompasses eight diverse machine learning (ML) algorithms, spanning from K-Nearest Neighbors (KNN) to Support Vector Machines (SVM), including notable contenders such as LightGBM (LGBM), CatBoost, XGBoost, decision trees (DT), random forests (RF), and multinomial naive Bayes (MultinomialNB). This rigorous examination illuminates a distinguished model, achieving a remarkable accuracy rate of 99.683% on the LC25000 dataset. The extension of this methodology to breast cancer detection, utilizing the BreakHis dataset, yields a commendable accuracy rate of 94.808%. These findings underscore the transformative potential of AI in the intricate landscape of histopathological analysis, positioning it as a pivotal force in advancing diagnostic capabilities. A meticulous comparative analysis not only underscores the merits but also elucidates the limitations of existing AI applications in medical imaging, thereby charting a roadmap for future refinements and clinical deployments. Consequently, continued research in AI within clinical settings is advocated, with the ultimate aim of fortifying early cancer diagnosis and subsequently enhancing patient outcomes through judicious therapeutic interventions.

Keywords

• Densenet201
• histopathological images
• image processing
• lung cancer
• machine learning