IV. CONCLUSION We have presented development steps toward a computer aided analysis and diagnosis system for oral mucosal lesions. Two common classifiers were evaluated on real data obtained from the lesion of the oral cavity. The obtained overall classification accuracy around 85% may be compared with the human performance. The diagnostic performance of the human specialist is estimated at 90%, while the diagnostic ability of dentists in general practice is estimated at 75%, when compared to a specialist [5]. Our current oral lesion database includes additional image samples from Indian population, such as erytroplakia and oral submucous fibrosis, which have high chances to develop into oral cancer. The lesions will be analysed as the number of cases will grow. The larger number of examples will give more accurate estimates of the classification performance. The advantage of standard color imaging is its simplicity and the low cost. The technique is also non-invasive, and sensitive for screening and early detection. Such technology will help the countries in South-East Asian region loaded with the highest number of oral cancer in the world [3]. It also will help in other parts of the world. However, due to the overlapping between the investigated classes, to improve the systems performance, probably we may need to incorporate additional imaging data from frequencies beyond the visual spectrum of the light. The developed method will be implemented as a lesion analysis tool and decision support system, and that could be used in India and other clinical settings elsewhere.
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