Duck Pack Optimization With Deep Transfer Learning-Enabled Oral Squamous Cell Carcinoma Classification on Histopathological Images
Earlier detection and classification of squamous cell carcinoma (OSCC) is a widespread issue for efficient treatment, enhancing survival rate, and reducing the death rate. Thus, it becomes necessary to design effective diagnosis models for assisting pathologists in the OSCC examination process. In r...
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| Published in | International journal of grid and high performance computing Vol. 15; no. 2; pp. 1 - 21 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Hershey
IGI Global
01.01.2023
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1938-0259 1938-0267 1938-0267 |
| DOI | 10.4018/IJGHPC.320474 |
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| Abstract | Earlier detection and classification of squamous cell carcinoma (OSCC) is a widespread issue for efficient treatment, enhancing survival rate, and reducing the death rate. Thus, it becomes necessary to design effective diagnosis models for assisting pathologists in the OSCC examination process. In recent times, deep learning (DL) models have exhibited considerable improvement in the design of effective computer-aided diagnosis models for OSCC using histopathological images. In this view, this paper develops a novel duck pack optimization with deep transfer learning enabled oral squamous cell carcinoma classification (DPODTL-OSC3) model using histopathological images. The goal of the DPODTL-OSC3 model is to improve the classifier outcomes of OSCC using histopathological images into normal and cancerous class labels. Finally, the variational autoencoder (VAE) model is utilized for the detection and classification of OSCC. The performance validation and comparative result analysis for the DPODTL-OSC3 model are tested using a histopathological imaging database. |
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| AbstractList | Earlier detection and classification of squamous cell carcinoma (OSCC) is a widespread issue for efficient treatment, enhancing survival rate, and reducing the death rate. Thus, it becomes necessary to design effective diagnosis models for assisting pathologists in the OSCC examination process. In recent times, deep learning (DL) models have exhibited considerable improvement in the design of effective computer-aided diagnosis models for OSCC using histopathological images. In this view, this paper develops a novel duck pack optimization with deep transfer learning enabled oral squamous cell carcinoma classification (DPODTL-OSC3) model using histopathological images. The goal of the DPODTL-OSC3 model is to improve the classifier outcomes of OSCC using histopathological images into normal and cancerous class labels. Finally, the variational autoencoder (VAE) model is utilized for the detection and classification of OSCC. The performance validation and comparative result analysis for the DPODTL-OSC3 model are tested using a histopathological imaging database. |
| Audience | Academic |
| Author | Patil, Annapurna P Shetty, Savita K |
| AuthorAffiliation | Department of Computer Science and Engineering, Ramaiah Institute of Technology, Bangalore, India & Visvesvaraya Technological University, Belagavi, India |
| AuthorAffiliation_xml | – name: Department of Computer Science and Engineering, Ramaiah Institute of Technology, Bangalore, India & Visvesvaraya Technological University, Belagavi, India |
| Author_xml | – sequence: 1 givenname: Savita surname: Shetty middlename: K fullname: Shetty, Savita K organization: Department of Computer Science and Engineering, Ramaiah Institute of Technology, Bangalore, India & Visvesvaraya Technological University, Belagavi, India – sequence: 2 givenname: Annapurna surname: Patil middlename: P fullname: Patil, Annapurna P organization: Department of Computer Science and Engineering, Ramaiah Institute of Technology, Bangalore, India & Visvesvaraya Technological University, Belagavi, India |
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| SubjectTerms | CAD Computer aided design Deep learning Diagnosis Image classification Machine learning Mortality Optimization Squamous cell carcinoma |
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| Title | Duck Pack Optimization With Deep Transfer Learning-Enabled Oral Squamous Cell Carcinoma Classification on Histopathological Images |
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