Cancer Diagnosis through Contour Visualization of Gene Expression Leveraging Deep Learning Techniques

Vinoth Kumar Venkatesan; Karthick Raghunath Kuppusamy Murugesan; Kaladevi Amarakundhi Chandrasekaran; Mahesh Thyluru Ramakrishna; Surbhi Bhatia Khan; Ahlam Almusharraf; Abdullah Albuali

doi:10.3390/diagnostics13223452

Cancer Diagnosis through Contour Visualization of Gene Expression Leveraging Deep Learning Techniques

Diagnostics (Basel). 2023 Nov 15;13(22):3452. doi: 10.3390/diagnostics13223452.

Authors

Vinoth Kumar Venkatesan¹, Karthick Raghunath Kuppusamy Murugesan², Kaladevi Amarakundhi Chandrasekaran³, Mahesh Thyluru Ramakrishna², Surbhi Bhatia Khan^{4

5

6}, Ahlam Almusharraf⁷, Abdullah Albuali⁸

Affiliations

¹ School of Computer Science Engineering and Information Systems (SCORE), Vellore Institute of Technology, Vellore 632014, India.
² Department of Computer Science and Engineering, Faculty of Engineering and Technology, JAIN (Deemed-to-be University), Bangalore 562112, India.
³ Department of Computer Science and Engineering, Sona College of Technology, Salem 636005, India.
⁴ Department of Data Science, School of Science Engineering and Environment, University of Salford, Manchester M5 4WT, UK.
⁵ Department of Engineering and Environment, University of Religions and Denominations, Qom 37491-13357, Iran.
⁶ Department of Electrical and Computer Engineering, Lebanese American University, Byblos P.O. Box 13-5053, Lebanon.
⁷ Department of Business Administration, College of Business and Administration, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia.
⁸ Department of Computer Science, School of Computer Science and Information Technology, King Faisal University, Hofuf 11671, Saudi Arabia.

Abstract

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.

Keywords: accuracy; cancer; classification; computation; contour; detection; diagnosis; loss; precision; recall; visualization.

Grants and funding

This research is supported by Princess Nourah Bint Abdulrahman University Researchers Supporting Project number (PNURSP2023R432), Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia.