Computer-assisted medical image classification for early diagnosis of oral cancer employing deep learning algorithm
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....
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| Vydáno v: | Journal of cancer research and clinical oncology Ročník 145; číslo 4; s. 829 - 837 |
|---|---|
| Hlavní autoři: | , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Berlin/Heidelberg
Springer Berlin Heidelberg
01.04.2019
Springer Nature B.V |
| Témata: | |
| ISSN: | 0171-5216, 1432-1335, 1432-1335 |
| On-line přístup: | Získat plný text |
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| 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. |
|---|---|
| AbstractList | 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. PurposeOral 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.MethodsTo 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.ResultsThe 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.ConclusionsWe 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. 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.PURPOSEOral 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.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.METHODSTo 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.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.RESULTSThe 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.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.CONCLUSIONSWe 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. 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. 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. 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. 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. |
| Author | Samuel Nadar, Edward Rajan Jeyaraj, Pandia Rajan |
| Author_xml | – sequence: 1 givenname: Pandia Rajan orcidid: 0000-0001-7086-6596 surname: Jeyaraj fullname: Jeyaraj, Pandia Rajan email: pandiarajan@mepcoeng.ac.in organization: Department of Electrical and Electronics Engineering, Mepco Schlenk Engineering College (Autonomous) – sequence: 2 givenname: Edward Rajan surname: Samuel Nadar fullname: Samuel Nadar, Edward Rajan organization: Department of Electrical and Electronics Engineering, Mepco Schlenk Engineering College (Autonomous) |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/30603908$$D View this record in MEDLINE/PubMed |
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| ContentType | Journal Article |
| Copyright | Springer-Verlag GmbH Germany, part of Springer Nature 2019 Journal of Cancer Research and Clinical Oncology is a copyright of Springer, (2019). All Rights Reserved. Copyright Springer Nature B.V. Apr 2019 Springer-Verlag GmbH Germany, part of Springer Nature 2019 2019 |
| Copyright_xml | – notice: Springer-Verlag GmbH Germany, part of Springer Nature 2019 – notice: Journal of Cancer Research and Clinical Oncology is a copyright of Springer, (2019). All Rights Reserved. – notice: Copyright Springer Nature B.V. Apr 2019 – notice: Springer-Verlag GmbH Germany, part of Springer Nature 2019 2019 |
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| Keywords | Hyperspectral image data Image labeling Medical image classification Oral cancer diagnosis Deep learning algorithm |
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| PublicationTitle | Journal of cancer research and clinical oncology |
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| References | Baljit Singh, Singh (CR1) 2016; 17 He, Ma (CR10) 2012 Christodoulidis, Anthimopoulos, Ebner (CR3) 2017; 21 Murray, Rourke, Hogan, Fenton (CR22) 2016; 54 Mathews, Kambhamettu, Barner (CR21) 2018; 99 Chudgar, Conant, Weinstein (CR4) 2017; 72 Ge, Wang, Liu, Li (CR8) 2018; 12 Palsson, Sveinsson, Ulfarsson (CR24) 2017; 14 Lustberg, van Soest, Mark (CR20) 2018; 126 Jie, Shufang, Xizhao, Guoqing, Liyan (CR15) 2018; 12 Hinton, Deng, Yu (CR13) 2012; 29 Kourou, Exarchos, Exarchos (CR19) 2015; 13 Yuan, Lin, Wang (CR28) 2015; 46 Bradley, Erickson, Panagiotis (CR2) 2018; 15 Deepak Kumar, Surendra Bilouhan, Kumar (CR5) 2018; 25 Wang, Gong, Yu (CR27) 2017; 36 Zhihuai, Zhenhua, Chengshan (CR29) 2018; 12 Dey, Chatterjee, Dalai, Munshi, Chakravorti (CR6) 2017; 24 Kalantari, Ramamoorthi (CR17) 2017; 36 Philippe, Vincent, Christophe, Alex (CR25) 2018; 98 Hijazi, Chan (CR12) 2012; 4 Huang, Xiao, Wei (CR14) 2015; 12 Heba, El-Dahshan, El-Horbaty, Abdel-Badeeh (CR11) 2018; 3 Gregory, Way, Sanchez (CR9) 2018; 23 Kiranyaz, Ince, Gabbouj (CR18) 2016; 63 Prochzazka, Vaseghi, Charvatova (CR26) 2017; 7 Ordonez, Roggen (CR23) 2016; 16 Dou, Chen, Yu (CR7) 2016; 35 Jin, McCann, Froustey (CR16) 2017; 26 AV Chudgar (2834_CR4) 2017; 72 P Gregory (2834_CR9) 2018; 23 Y Yuan (2834_CR28) 2015; 46 N Kalantari (2834_CR17) 2017; 36 C Wang (2834_CR27) 2017; 36 H Hijazi (2834_CR12) 2012; 4 J Bradley (2834_CR2) 2018; 15 H He (2834_CR10) 2012 W Huang (2834_CR14) 2015; 12 KH Jin (2834_CR16) 2017; 26 S Kiranyaz (2834_CR18) 2016; 63 S Christodoulidis (2834_CR3) 2017; 21 D Dey (2834_CR6) 2017; 24 Q Dou (2834_CR7) 2016; 35 X Zhihuai (2834_CR29) 2018; 12 T Lustberg (2834_CR20) 2018; 126 M Philippe (2834_CR25) 2018; 98 FJ Ordonez (2834_CR23) 2016; 16 M Heba (2834_CR11) 2018; 3 G Hinton (2834_CR13) 2012; 29 F Palsson (2834_CR24) 2017; 14 H Ge (2834_CR8) 2018; 12 A Prochzazka (2834_CR26) 2017; 7 K Baljit Singh (2834_CR1) 2016; 17 Z Jie (2834_CR15) 2018; 12 K Kourou (2834_CR19) 2015; 13 SM Mathews (2834_CR21) 2018; 99 G Murray (2834_CR22) 2016; 54 J Deepak Kumar (2834_CR5) 2018; 25 |
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Oral cancer is a complex wide spread cancer, which has high severity. Using advanced technology and deep learning algorithm early detection and... Oral cancer is a complex wide spread cancer, which has high severity. Using advanced technology and deep learning algorithm early detection and classification... PurposeOral cancer is a complex wide spread cancer, which has high severity. Using advanced technology and deep learning algorithm early detection and... |
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| SubjectTerms | 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 |
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| Title | Computer-assisted medical image classification for early diagnosis of oral cancer employing deep learning algorithm |
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