Abstract
Despite the fact that current classical image classification techniques have been widely used to address real-world problems, these applications are frequently hampered by problems including disappointing results, poor classification accuracy, and constrained adaptability. This method separates the mammography image tumour feature extraction and classification processes. The effective way to improve the accuracy of mammography image tumour classification is to integrate the feature extraction and classification processes using the potent deep learning model. The deep structural advantages of multilayer nonlinear mapping and the tumour representation of well-multidimensional data linear decomposition, however, are fully utilised in this paper, which also introduces the idea of tumour representation based on shape-based feature extraction into the architecture of the deep learning network.
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