Galactic swarm optimization with deep transfer learning driven colorectal cancer classification for image guided intervention

• Develop an optimal deep learning based colorectal cancer classifier model. Present Adam optimizer with MobileNet-based feature extractor. Employ galactic swarm optimization LSTM model for classification. Validate histopathological images from the Warwick-QU database. In this era of “precision” medicine, image-guided intervention (IGI) enables real-time customized and accurate treatment using imaging phenotype-based approaches. Colorectal (CC) is third most commonly occurring cancer, resulting in nearly 10% cases over globe. classification (CCC) by artificial intelligence (AI) approaches not only enhances accuracy results but also allows physicians to make prompt decisions. view, article introduces a novel Galactic Swarm Optimization Deep Transfer Learning Driven Cancer Classification (GSODTL-C3M) IGI. The primary aim GSODTL-C3M appropriately categorize test into existence CC. To accomplish this, presented employs image pre-processing bilateral filtering (BF) technique remove noise. Besides, MobileNet applied as Finally, GSO methodology long short-term memory (LSTM) employed recognize classify An extensive range simulations was taken place, stated advanced performance current state art methodologies.