Enhancing Breast Cancer Detection in Mammography Using Firefly Algorithm-Based Image Enhancement Techniques
Keywords:
Mammography, Breast cancer detection, early disease detection, predictionAbstract
Early identification is essential for effective treatment and improved patient outcomes in breast cancer, which continues to be a prevalent worldwide health concern. Widely used as a screening tool, mammography is crucial in the detection of breast abnormalities. Image quality is crucial to the success of mammography, but it can be impaired by things like tissue density and the technical limits of imaging equipment. In light of this difficulty, the current research presents a fresh strategy for improving breast cancer diagnosis in mammography pictures by incorporating the Firefly Algorithm-based image enhancing techniques.The Firefly technique is a potent optimisation technique that boosts image quality by increasing contrast and decreasing noise; it gets its name from the natural phenomenon of fireflies blinking. Using this method, we present an all-encompassing framework for enhancing mammography images. The proposed method enhances the overall image quality while also making small breast lesions more visible by optimising a number of characteristics, including brightness, contrast, and sharpness.We performed extensive experiments on a large collection of mammography images to determine the efficacy of our approach. Our Firefly Algorithm-based solution regularly outperforms conventional enhancement methods in terms of lesion diagnosis and picture quality improvement, as shown by a comparison with existing methods. We believe that our approach has the potential to considerably boost the accuracy of breast cancer diagnosis in mammography, particularly in cases where subtle or low-contrast abnormalities may be missed by current techniques.
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