Harnessing deep learning for enhanced ‎detection of thymic epithelial tumors

Authors

  • heena Kousar Associate Professor, Computer Science and Engineering, East Point College of Engineering and Technology, Jnanaprabha campus, ‎Bengaluru-560049
  • Dr. Geetha T V Assistant Professor, Department of Computer Science and Engineering, SRM Institute of Science and Technology, Ramapuram, Chennai, ‎Tamil Nadu
  • Syamala N Assistant Professor, Vallurupally Nageshwara Rao Vignana Jyothi Institute of Engineering and Technology, Hyderabad, Telangana, India
  • Dr.Arvind Kumar Shukla Professor, Department of Computer Applications, IFTM University, Moradabad, India
  • Dr.Venkatesh N Assistant Professor, School of CS & AI-CSE, S R University-Warangal-506371
  • Kumar Kusumanchi T.P.S Assistant professor, Department of IoT, Koneru Lakshamaiah Education Foundation, Green fields, Vaddeswaram, Guntur, AP, India-‎‎522502

How to Cite

Kousar, heena, T V, D. G. ., N, S., Shukla, D. K., N, D., & T.P.S, K. K. (2025). Harnessing deep learning for enhanced ‎detection of thymic epithelial tumors. International Journal of Basic and Applied Sciences, 14(1), 136-147. https://doi.org/10.14419/p2j8w965

Received date: March 26, 2025

Accepted date: April 26, 2025

Published date: May 2, 2025

DOI:

https://doi.org/10.14419/p2j8w965

Keywords:

Self-Supervised Learning; Contrastive Learning; Adaptive Neuro-Evolution; Deep Learning; Feature Selection; Tumor Classification; TCGA-THYM; SimCLR; Moco; Medical Imaging

Abstract

The increasing reliance on deep learning for medical image classification has significantly improved tumor detection. However, ‎traditional deep learning models often suffer from redundant feature extraction, poor feature separability, and suboptimal classifica-tion ‎performance. This study proposes an advanced hybrid framework integrating Adaptive Neuro-Evolutionary ‎Pruned Embedding (ANPE) and Self-Supervised Embedding Transformation (SSET) for tumor classification to overcome these limitations. The ANPE module ‎optimizes feature selection by employing multi-scale feature extraction, neuro-evolutionary pruning, and self-attention ranking to retain only ‎the most discriminative features, thus enhancing classification accuracy. The SSET module, on the other hand, leverages contrastive learning ‎techniques, specifically SimCLR (Simple Contrastive Learning) and MoCo (Momentum Con-trastive Learning), to improve feature ‎representation and cluster separation. The proposed ANPE-SSET framework was evaluated on the TCGA-THYM dataset, a benchmark ‎dataset for histopathological tumor classification. Experimental results demonstrate that ANPE-SSET outperforms baseline models such as ‎CNN, ResNet, and Vision Transformers (ViTs). The proposed model achieved an accuracy of 96.2%, significantly surpassing CNN ‎‎(85.3%), ResNet (90.5%), and ViT (92.1%). The AUC-ROC score of 98.3% high-lights its superior ability to distinguish between tumor ‎classes. In conclusion, the ANPE-SSET hybrid model effectively integrates evolutionary feature selection and self-supervised contrastive ‎learning, leading to state-of-the-art performance in tumor classifica-tion. The results demonstrate its superiority over traditional models, ‎making it a promising approach for medical image analysis. Future work will focus on extending this framework to other medical imaging ‎datasets and further optimizing it for clinical applica-tions‎.

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How to Cite

Kousar, heena, T V, D. G. ., N, S., Shukla, D. K., N, D., & T.P.S, K. K. (2025). Harnessing deep learning for enhanced ‎detection of thymic epithelial tumors. International Journal of Basic and Applied Sciences, 14(1), 136-147. https://doi.org/10.14419/p2j8w965

Received date: March 26, 2025

Accepted date: April 26, 2025

Published date: May 2, 2025