Carbon pattern in polymeric nanofabrication for breast tumor molecular cell analysis using hybrid machine learning technique

New innovations in microscopic or molecular profiling methods that provide a high level of information with regard to either spatial or molecular properties, but typically not both, have been a major driver of recent advancements in cancer research and diagnoses. The first malignant tumour to develop in women is now breast cancer. The best way to enhance a breast cancer patient's prognosis is through early identification and treatment. The qualitative differential diagnosis of breast nodules is crucial for detection as well as diagnosis of breast cancer. Importance of breast MRI is growing as a result of the quick advancement of MRI technology, particularly the use of high field strength and ultra-high field strength. This research proposes novel technique in carbon pattern based polymeric nanofabrication in breast image based on contrast improvement and feature extraction with training using machine learning techniques. Here the input breast image has been analysed for molecular cell analysis by nano material by segmentation using curvelet multi-interval histogram normalization. Then the segmented image features are extracted using hybrid weighted regularized spatial Boltzmann machine architectures. Experimental analysis is carried out based on various breast image dataset in terms of random accuracy, sensitivity, AUC, F-measure, dice coefficient, NSE.