Authors - Arpita Mahakalkar, Karan Deshmukh, Kruthika Agarwal, Sudhanshu Maurya, Firdous Sadaf M. Ismail, Rachit Garg Abstract - Breast cancer remains a driving cause of mortality among ladies around the world, emphasizing the requirement for progressed location strategies. This considers creating a novel Computer-Aided Conclusion (CAD) framework leveraging MATLAB to progress the precision and proficiency of breast cancer location. The framework utilizes mammographic pictures and applies progressed measurable extraction procedures to analyze key characteristics, such as mass shape and edges. These highlights assist in classifying utilizing machine learning calculations, counting neural systems, and back vector machines. A one of a kind integration of wavelet change and multilayer perceptron strategies illustrated critical enhancements in recognizing Incendiary Breast Cancer (IBC) from non-IBC cases. The proposed approach beats conventional strategies, advertising improved symptomatic unwavering quality, decreased execution time, and tall exactness in cancer classification. This work underscores the potential of joining progressed machine learning procedures and picture-preparing apparatuses within the early and exact location of breast cancer, eventually supporting radiologists and decreasing demonstrative challenges.
