Adaptive Crossover Boosted Reptile Search Optimized Transformer Framework for Accurate Lung Tumor Segmentation

The computed tomography enables accurate lung tumor identification and early treatment planning, but existing studied face challenges inclusing image quality variations, tumor complexity and performance degradation. Therefore, an Adaptive Crossover Boosted Reptile Search optimized Adaptive Modified Generator Cross Transformer model is proposed in this paper for segmenting lung tumors by overcoming these limitations. The images are collected from diverse data sources that are pre-processed to enhance generalization ability as well as image quality while minimizing overfitting issues. The Modified Atrous Convolutional Cross Transformer model is applied as a feature extractor that has the ability to efficiently extract local as well as global features. For the segmentation task, the Improved Generator-Discriminator model is introduced that uses UNet as a base model and skip connections to preserve detailed spatial information. An Adaptive Crossover Boosted Reptile Search Optimization algorithm is implemented for optimizing hyperparameters by integrating crossover and adaptive weighting strategies for avoiding local optimal issues. Different kinds of experimental validations are performed to analyze the superior performance of the proposed model. Among all analyses, the proposed model attained the highest prediction accuracy of 98.71 ± 0.18% and a Dice score of 0.9866 ± 0.0018.