Year: 2025
Type: article
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.
DOI: 10.14419/p2j8w965
Authors: Heena Kousar, Dr. Geetha T V, Syamala N, Dr.Arvind Kumar Shukla, Dr.Venkatesh N, Kumar Kusumanchi T.P.S
Language: English
Topic: AI in cancer detection
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4410058293