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

• Published in: Journal of cancer research and clinical oncology Vol. 145; no. 4; pp. 829 - 837
• Main Authors: Jeyaraj, Pandia Rajan, Samuel Nadar, Edward Rajan
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.04.2019; Springer Nature B.V
• Subjects: Accuracy; Algorithms; Cancer Research; Cancer therapies; Classification; Deep Learning; Diagnosis; Early Detection of Cancer - methods; Hematology; Humans; Image processing; Image Processing, Computer-Assisted - methods; Internal Medicine; Medicine; Medicine & Public Health; Mouth Neoplasms - diagnostic imaging; Neural networks; Oncology; Oral cancer; Original Article – Cancer Research; Original – Cancer Research; Tumors; Hyperspectral image data; Image labeling; Medical image classification; Oral cancer diagnosis; Deep learning algorithm
• ISSN: 0171-5216; 1432-1335; 1432-1335
• DOI: 10.1007/s00432-018-02834-7

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.