Hyper-parameter Tuning of U-Net to Breast Pectoral Muscle Segmentation and Classification using Pre-Trained Models
Keywords:
Segmentation, Breast Cancer, Pectoral Muscle, Mammogram, Hyper-parameters, ClassificationAbstract
In medical image processing, segmentation is essential for identifying and analyzing various body sections, including organs and tumors inside of them. This can support the diagnosis and treatment planning process, perhaps saving many lives. To obtain a more precise interpretation of the image, breast pectoral muscles must be removed, among other intricate pre-processes involved in the detection of breast cancer. The most popular deep learning architectures for image segmentation are SegNet and U-Net; however, fine-tuning hyperparameters will yield more accurate results from these networks. This research work combines Particle Swarm Optimization with Grey Wolf Optimization to address issues with the U-Net's hyper-parameter tuning for the excision of the breast pectoral muscle. The epoch cycles, learning ratio, Dropout Ratio, number of kernels, and activation function—all of which are hyper-parameters of the U-Net network are optimized. In comparison to traditional U-Net, U-Net with Grey Wolf, and U-Net with PSO, which show accuracies of 77.19%, 84.76%, and 89.13%, respectively, the suggested model achieved an accuracy rate of 94.28%. After segmenting the photos, the DenseNet201, EfficientNetB5, ResNet101, and VGG19 ImageNet models are used to classify them. These models produce classification accuracies of 92.01%, 97.48%, 89.56%, and 93.32%, respectively.
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