Advancing early detection of chromophobe renal cell carcinoma: a Bayesian optimization approach to machine learning models

Improving chromophobe renal cell carcinoma (ChRCC) prediction for a more accurate diagnosis is the aim of this research. This research focuses on enhancing ChRCC detection via image analysis, thereby overcoming the huge difficulty of obtaining and early and accurate diagnosis, given the terrible reality of kidney cancer, particularly ChRCC, and the critical role that early detection and diagnosis play in patient survival. Conventional imaging methods could not be definitive, which could result in a delayed or incorrect diagnosis. The efficacy of conventional machine learning and feature extraction techniques in precise diagnosis is limited by the hidden nonlinear dynamics that ChRCC imaging patterns frequently display. The detection and prognosis of ChRCC were improved by the computation of the most pertinent features, hybrid feature extraction, key feature selection, and optimization of machine learning algorithms through innovative Bayesian optimization. This allowed for effective early decision-making and improved decision support systems in general. This research is organized into multiple stages. Image enhancement methods and data augmentation were used to improve imaging pre-processing in the initial stage. In the second stage of feature engineering, the most pertinent features were found using the gray-level co-occurrence matrix (GLCM). In the third stage, Bayesian optimization was used to optimize the machine learning algorithms’ hyperparameters. Lastly, strong assessment indicators were used to assess the model’s performance. With 99.02% accuracy, 98.19% F1-Score, 98.04% Matthews Correlation Coefficient (MCC), and 0.9989 Area Under the Curve (AUC), a large neural network yielded the best results. Similar results were obtained using a medium neural network, which had an accuracy of 98.55% and an AUC of 0.9976. These outstanding results demonstrate that the suggested approach has the potential to revolutionize the treatment of kidney chromophobe carcinoma by improving the condition’s detection, diagnosis, and comprehension, which would eventually enhance patient outcomes and the efficiency of the healthcare system.