Computer-assisted medical image classification for early diagnosis of oral cancer employing deep learning algorithm View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-04

AUTHORS

Pandia Rajan Jeyaraj, Edward Rajan Samuel Nadar

ABSTRACT

PURPOSE: Oral cancer is a complex wide spread cancer, which has high severity. Using advanced technology and deep learning algorithm early detection and classification are made possible. Medical imaging technique, computer-aided diagnosis and detection can make potential changes in cancer treatment. In this research work, we have developed a deep learning algorithm for automated, computer-aided oral cancer detecting system by investigating patient hyperspectral images. METHODS: To validate the proposed regression-based partitioned deep learning algorithm, we compare the performance with other techniques by its classification accuracy, specificity, and sensitivity. For the accurate medical image classification objective, we demonstrate a new structure of partitioned deep Convolution Neural Network (CNN) with two partitioned layers for labeling and classify by labeling region of interest in multidimensional hyperspectral image. RESULTS: The performance of the partitioned deep CNN was verified by classification accuracy. We have obtained classification accuracy of 91.4% with sensitivity 0.94 and a specificity of 0.91 for 100 image data sets training for task classification of cancerous tumor with benign and for task classification of cancerous tumor with normal tissue accuracy of 94.5% for 500 training patterns was obtained. CONCLUSIONS: We compared the obtained results from another traditional medical image classification algorithm. From the obtained result, we identify that the quality of diagnosis is increased by proposed regression-based partitioned CNN learning algorithm for a complex medical image of oral cancer diagnosis. More... »

PAGES

829-837

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00432-018-02834-7

DOI

http://dx.doi.org/10.1007/s00432-018-02834-7

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1111057430

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/30603908


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