Hybrid machine learning‐based breast cancer segmentation framework using ultrasound images with optimal weighted features

• Published in: Cell biochemistry and function Vol. 42; no. 4; pp. e4054 - n/a
• Main Authors: Vijayan, Sudharsana, Panneerselvam, Radhika, Roshini, Thundi Valappil
• Format: Journal Article
• Language: English
• Published: England Wiley Subscription Services, Inc 01.06.2024
• Subjects: Algorithms; Breast cancer; breast cancer detection; Breast Neoplasms - diagnostic imaging; Breast Neoplasms - pathology; conglomerated aphid with galactic swarm optimization; Datasets; Feature extraction; Female; Human error; Humans; Image acquisition; Image contrast; Image enhancement; Image processing; Image Processing, Computer-Assisted; Image segmentation; Imaging techniques; Learning algorithms; Machine Learning; Mammography; Medical imaging; optimal weighted feature selection; Optimization; Performance enhancement; Pixels; Radiation; Segmentation; Ultrasonic imaging; Ultrasonography; Ultrasound; ultrasound images; breast cancer detection; optimal weighted feature selection; ultrasound images; conglomerated aphid with galactic swarm optimization
• ISSN: 0263-6484; 1099-0844; 1099-0844
• DOI: 10.1002/cbf.4054

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. Significance statement Nowadays the breast cancer is the leading cause of death in women due to failure to detect at early stage. The Mammogram is an advanced method to detect the breast cancer but this cause radiation but ultrasound modality is widely preferred in young and pregnant women for avoiding radiation. The Conglomerated Aphid with Galactic Swarm Optimization (CAGSO) model represents a promising step forward in the field of breast cancer detection, these findings not only contribute to the ongoing progress in breast cancer detection but also highlight the potential of innovative optimization methods, such as CAGSO to elevate the standards of diagnostic algorithms. As we continue to refine and validate these models with larger datasets, the practical implications of such advancements in clinical settings become increasingly promising one.