Automated Detection of Oral Pre-Cancerous Tongue Lesions Using Deep Learning for Early Diagnosis of Oral Cavity Cancer. (24th November 2020)

• Record Type: Journal Article
• Authors: Shamim, Mohammed Zubair M; Syed, Sadatullah; Shiblee, Mohammad; Usman, Mohammed; Ali, Syed Jaffar; Hussein, Hany S; Farrag, Mohammed
• Is Part Of: Computer journal. Volume 65:Number 1(2022)
• Page Start: 91
• Page End: 104
• Publication Date: 2020-11-24
• Subjects: oral cavity cancer -- diagnosis -- tongue lesions -- deep convolutional neural network -- transfer learning -- artificial intelligence; Computers -- Periodicals; 005.1
• DOI: 10.1093/comjnl/bxaa136
• Languages: English
• ISSNs: 0010-4620

Abstract

Abstract: Discovering oral cavity cancer (OCC) at an early stage is an effective way to increase patient survival rate. However, current initial screening process is done manually and is expensive for the average individual, especially in developing countries worldwide. This problem is further compounded due to the lack of specialists in such areas. Automating the initial screening process using artificial intelligence (AI) to detect pre-cancerous lesions can prove to be an effective and inexpensive technique that would allow patients to be triaged accordingly to receive appropriate clinical management. In this study, we have applied and evaluated the efficacy of six deep convolutional neural network (DCNN) models using transfer learning, for identifying pre-cancerous tongue lesions directly using a small dataset of clinically annotated photographic images to diagnose early signs of OCC. DCNN models were able to differentiate between benign and pre-cancerous tongue lesions and were also able to distinguish between five types of tongue lesions, i.e. hairy tongue, fissured tongue, geographic tongue, strawberry tongue and oral hairy leukoplakia with high classification performances. Preliminary results using an (AI + Physician) ensemble model demonstrate that an automated pre-screening process of oral tongue lesions using DCNNs can achieve 'near-human' level classification performance for diagnosing early signs of OCC in patients.