MSPO: A machine learning hyperparameter optimization method for enhanced breast cancer image classification

Abstract

As one of the major threats to women's health worldwide, breast cancer requires early diagnosis and accurate classification, since they are key to optimizing therapeutic interventions and ensuring precise prognosis. Recently, deep learning has demonstrated notable advantages in breast cancer image classification. However, their performance heavily relies on the proper configuration of hyperparameters. To overcome the inefficiencies and weaknesses of conventional hyperparameter optimization methods, like limited effectiveness and vulnerability to premature convergence, this research proposes a Multi-Strategy Parrot Optimizer (MSPO) and applies it to breast cancer image classification tasks. Based on the original Parrot Optimizer, MSPO integrates several strategies, including Sobol sequence initialization, nonlinear decreasing inertia weight, and a chaotic parameter to enhance global exploration ability and convergence steadiness. Tests using the CEC 2022 benchmark functions reveal that MSPO surpasses leading algorithms regarding optimization precision and convergence rate. An ablation study was conducted on three variants of MSPO through CEC 2022 to further validate the effectiveness of each key strategy. Furthermore, MSPO is combined with the ResNet18 model and applied to the BreaKHis breast cancer image dataset. Results indicate that the model optimized by MSPO notably surpasses both the non-optimized version and other alternative optimization algorithms using four assessment indicators: accuracy, precision, recall, and F1-score. This validates the promising application potential and practical significance of MSPO in medical image classification tasks.

Keywords: Multi-strategy parrot optimizer; breast cancer; hyperparameter optimization; image classification; machine learning.

Figure 1.

The four behaviors of parrots where a is foraging, b is staying, c is communicating, and d is fear of strangers.

Figure 2.

Distribution of the population under distinct initialization schemes where a is scatterplot of population distribution with random initialization, and b is scatterplot of population distribution with Sobol sequence initialization.

Figure 3.

Variation curve of the inertia weight w over iterations.

Figure 4.

Time series plot of Tent map.

Figure 5.

Probability distribution histogram of Tent map.

Figure 6.

Flowchart of MSPO.

Figure 7.

3D surface plots of the CEC 2022 benchmark functions.

Figure 8.

Convergence curve of algorithms where a is F1, b is F5, c is F6, and d is F11.

Figure 9.

Sample images of the BreaKHis 400X dataset where a is Benign, and b is malignant.

Figure 10.

Histogram of the results of each model on four evaluation metrics.