A machine learning approach for identification of head and neck squamous cell carcinoma

Abstract

Squamous cell carcinoma is the most common type of head and neck cancer affecting about 30,000 Americans each year [1]. Diagnosis of tumor is backed by histopathologic examination of excised tissue in which lesion is speckled. Computer vision systems have yet to contribute significantly to the investigation of tumor areas in terms of histological slide analysis. Recently the improvements in imaging techniques led to the discovery of virtual histological slides. Virtual slides are of sufficiently high quality to generate immense interest within the research community. We describe a novel method to tackle automatic delineation of head and neck squamous cell carcinoma problem in virtual histological slides. A density-based clustering algorithm improved in this study plays a key role in the determination of the proliferative cell nuclei. The experimental results on high-resolution head and neck slides show that the proposed algorithm performed well, obtaining an average of 96% accuracy. © 2007 IEEE.