Cancer Diagnosis through Contour Visualization of Gene Expression Leveraging Deep Learning Techniques
Prompt diagnostics and appropriate cancer therapy necessitate the use of gene expression databases. The integration of analytical methods can enhance detection precision by capturing intricate patterns and subtle connections in the data. This study proposes a diagnostic-integrated approach combining...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-11-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/13/22/3452 |
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| author | Vinoth Kumar Venkatesan Karthick Raghunath Kuppusamy Murugesan Kaladevi Amarakundhi Chandrasekaran Mahesh Thyluru Ramakrishna Surbhi Bhatia Khan Ahlam Almusharraf Abdullah Albuali |
| author_facet | Vinoth Kumar Venkatesan Karthick Raghunath Kuppusamy Murugesan Kaladevi Amarakundhi Chandrasekaran Mahesh Thyluru Ramakrishna Surbhi Bhatia Khan Ahlam Almusharraf Abdullah Albuali |
| author_sort | Vinoth Kumar Venkatesan |
| collection | DOAJ |
| description | Prompt diagnostics and appropriate cancer therapy necessitate the use of gene expression databases. The integration of analytical methods can enhance detection precision by capturing intricate patterns and subtle connections in the data. This study proposes a diagnostic-integrated approach combining Empirical Bayes Harmonization (EBS), Jensen–Shannon Divergence (JSD), deep learning, and contour mathematics for cancer detection using gene expression data. EBS preprocesses the gene expression data, while JSD measures the distributional differences between cancerous and non-cancerous samples, providing invaluable insights into gene expression patterns. Deep learning (DL) models are employed for automatic deep feature extraction and to discern complex patterns from the data. Contour mathematics is applied to visualize decision boundaries and regions in the high-dimensional feature space. JSD imparts significant information to the deep learning model, directing it to concentrate on pertinent features associated with cancerous samples. Contour visualization elucidates the model’s decision-making process, bolstering interpretability. The amalgamation of JSD, deep learning, and contour mathematics in gene expression dataset analysis diagnostics presents a promising pathway for precise cancer detection. This method taps into the prowess of deep learning for feature extraction while employing JSD to pinpoint distributional differences and contour mathematics for visual elucidation. The outcomes underscore its potential as a formidable instrument for cancer detection, furnishing crucial insights for timely diagnostics and tailor-made treatment strategies. |
| first_indexed | 2024-03-09T16:54:08Z |
| format | Article |
| id | doaj.art-c914ef3db6ea4b5e8a7b58adb1af851e |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-09T16:54:08Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-c914ef3db6ea4b5e8a7b58adb1af851e2023-11-24T14:37:39ZengMDPI AGDiagnostics2075-44182023-11-011322345210.3390/diagnostics13223452Cancer Diagnosis through Contour Visualization of Gene Expression Leveraging Deep Learning TechniquesVinoth Kumar Venkatesan0Karthick Raghunath Kuppusamy Murugesan1Kaladevi Amarakundhi Chandrasekaran2Mahesh Thyluru Ramakrishna3Surbhi Bhatia Khan4Ahlam Almusharraf5Abdullah Albuali6School of Computer Science Engineering and Information Systems (SCORE), Vellore Institute of Technology, Vellore 632014, IndiaDepartment of Computer Science and Engineering, Faculty of Engineering and Technology, JAIN (Deemed-to-be University), Bangalore 562112, IndiaDepartment of Computer Science and Engineering, Sona College of Technology, Salem 636005, IndiaDepartment of Computer Science and Engineering, Faculty of Engineering and Technology, JAIN (Deemed-to-be University), Bangalore 562112, IndiaDepartment of Data Science, School of Science Engineering and Environment, University of Salford, Manchester M5 4WT, UKDepartment of Business Administration, College of Business and Administration, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi ArabiaDepartment of Computer Science, School of Computer Science and Information Technology, King Faisal University, Hofuf 11671, Saudi ArabiaPrompt diagnostics and appropriate cancer therapy necessitate the use of gene expression databases. The integration of analytical methods can enhance detection precision by capturing intricate patterns and subtle connections in the data. This study proposes a diagnostic-integrated approach combining Empirical Bayes Harmonization (EBS), Jensen–Shannon Divergence (JSD), deep learning, and contour mathematics for cancer detection using gene expression data. EBS preprocesses the gene expression data, while JSD measures the distributional differences between cancerous and non-cancerous samples, providing invaluable insights into gene expression patterns. Deep learning (DL) models are employed for automatic deep feature extraction and to discern complex patterns from the data. Contour mathematics is applied to visualize decision boundaries and regions in the high-dimensional feature space. JSD imparts significant information to the deep learning model, directing it to concentrate on pertinent features associated with cancerous samples. Contour visualization elucidates the model’s decision-making process, bolstering interpretability. The amalgamation of JSD, deep learning, and contour mathematics in gene expression dataset analysis diagnostics presents a promising pathway for precise cancer detection. This method taps into the prowess of deep learning for feature extraction while employing JSD to pinpoint distributional differences and contour mathematics for visual elucidation. The outcomes underscore its potential as a formidable instrument for cancer detection, furnishing crucial insights for timely diagnostics and tailor-made treatment strategies.https://www.mdpi.com/2075-4418/13/22/3452accuracyclassificationdetectiondiagnosiscontourvisualization |
| spellingShingle | Vinoth Kumar Venkatesan Karthick Raghunath Kuppusamy Murugesan Kaladevi Amarakundhi Chandrasekaran Mahesh Thyluru Ramakrishna Surbhi Bhatia Khan Ahlam Almusharraf Abdullah Albuali Cancer Diagnosis through Contour Visualization of Gene Expression Leveraging Deep Learning Techniques Diagnostics accuracy classification detection diagnosis contour visualization |
| title | Cancer Diagnosis through Contour Visualization of Gene Expression Leveraging Deep Learning Techniques |
| title_full | Cancer Diagnosis through Contour Visualization of Gene Expression Leveraging Deep Learning Techniques |
| title_fullStr | Cancer Diagnosis through Contour Visualization of Gene Expression Leveraging Deep Learning Techniques |
| title_full_unstemmed | Cancer Diagnosis through Contour Visualization of Gene Expression Leveraging Deep Learning Techniques |
| title_short | Cancer Diagnosis through Contour Visualization of Gene Expression Leveraging Deep Learning Techniques |
| title_sort | cancer diagnosis through contour visualization of gene expression leveraging deep learning techniques |
| topic | accuracy classification detection diagnosis contour visualization |
| url | https://www.mdpi.com/2075-4418/13/22/3452 |
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