Hybrid machine learning-based breast cancer segmentation framework using ultrasound images with optimal weighted...

One of the most dangerous conditions in clinical practice is breast cancer because it affects the entire life of women in recent days. Nevertheless, the existing techniques for diagnosing breast cancer are complicated, expensive, and inaccurate. Many trans-disciplinary and computerized systems are recently created to prevent human errors in both quantification and diagnosis. Ultrasonography is a crucial imaging technique for cancer detection. Therefore, it is essential to develop a system that enables the healthcare sector to rapidly and effectively detect breast cancer. Due to its benefits in predicting crucial feature identification from complicated breast cancer datasets, machine learning is widely employed in the categorization of breast cancer patterns. The performance of machine learning models is limited by the absence of a successful feature enhancement strategy. There are a few issues that need to be handled with the traditional breast cancer detection method. Thus, a novel breast cancer detection model is designed based on machine learning approaches and employing ultrasonic images. At first, ultrasound images utilized for the analysis is acquired from the benchmark resources and offered as the input to preprocessing phase. The images are preprocessed by utilizing a filtering and contrast enhancement approach and attained the preprocessed image. Then, the preprocessed images are subjected to the segmentation phase. In this phase, segmentation is performed by employing Fuzzy C-Means, active counter, and watershed algorithm and also attained the segmented images. Later, the segmented images are provided to the pixel selection phase. Here, the pixels are selected by the developed hybrid model Conglomerated Aphid with Galactic Swarm Optimization (CAGSO) to attain the final segmented pixels. Then, the selected segmented pixel is fed in to feature extraction phase for attaining the shape features and the textual features. Further, the acquired features are offered to the optimal weighted feature selection phase, and also their weights are tuned tune by the developed CAGSO. Finally, the optimal weighted features are offered to the breast cancer detection phase. Finally, the developed breast cancer detection model secured an enhanced performance rate than the classical approaches throughout the experimental analysis.