A manta ray-bayesian optimization approach for hyperparameter-tuned convolutional neural networks in lung cancer classification.

Lung cancer remains a global health challenge that is unavoidable. Despite the advances in lung cancer classification using deep learning models, the performance remains highly dependent on hyperparameter selection, whereas conventional grid or random search methods are often computationally inefficient in high-dimensional spaces. So, to address the issue, this paper presents a Convolutional Neural Network(CNN) which is hybridized by dual stage hyperparameter optimization techniques for lung cancer image classification. The approach integrates Bayesian Optimization (BO) and Manta Ray Foraging Optimization (MRFO) to efficiently explore and fine-tune a defined hyperparameter search space, including convolution filter count, learning rate, dense layer neurons, and dropout rate. Initially, Bayesian Optimization explores the search space by modeling the objective function with a Gaussian Process and selecting candidate hyperparameters via the Expected Improvement criterion. The best solution obtained is then further enhanced using MRFO, which incrementally refines the parameters through its chain, cyclone, and somersault foraging mechanisms. This two-step process strikes a balance between exploring the world and taking advantage of local resources. The CNN trained with the optimized hyperparameters achieved good accuracy in lung cancer image classification, demonstrating the potency of combining probabilistic modeling with bio-inspired optimization. Experimental results show that the proposed hybrid CNN method has a testing accuracy of 98%, which is better than that of many cutting-edge models. The results show that metaheuristic-based optimization could be useful in deep learning applications, especially in medical image analysis.