GWABHO-Adam: Grey Wolf Algorithm Based Hyperparameter Optimization of Adam to Enhance Cancer Classification from Microscopic Images

In recent years, Convolutional Neural Networks (CNN) have revolutionized the field of medical science by efficiently expediting the process of detection and classification of diseases such as cancer. Adam is one of the most popular techniques to fasten the optimization process of the gradient descent used by CNNs to converge toward the solution. However, manually tuning the hyperparameters of Adam and CNN is a tedious process as it requires continuous trial and error, and can lead to suboptimal performance. Such suboptimal training can lead to the wrong diagnosis of diseases, endangering the lives of patients. Hence, the presented work focuses on implementing the grey wolf optimizer, a nature-inspired metaheuristic optimization algorithm, for optimizing the hyperparameters of the Adam optimizer specific to each dataset. The classification methodology has been implemented using the MobileNet-V2model on three well-studied medical image datasets, viz. Warwick-QU, SIPaKMeD, and MHIST, where it has achieved a remarkable 2.5%, 6.83%, and 5.12% improvements in test accuracy over the base models without optimization, respectively. The proposed paradigm has been validated and its improved performance has been shown using the Mcnemar Test and SmoothGradCAM++ visualization. This clearly shows the efficacy of the proposed method in performing dataset-specific optimization to heighten the classification accuracy.