Currently,the improvement in AI is mainly related to deep learning techniques that are employed for the classification,identification,and quantification of patterns in clinical *** deep learning models show more remar...
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Currently,the improvement in AI is mainly related to deep learning techniques that are employed for the classification,identification,and quantification of patterns in clinical *** deep learning models show more remarkable performance than the traditional methods for medical image processing tasks,such as skin cancer,colorectal cancer,brain tumour,cardiac disease,Breast cancer(BrC),and a few *** manual diagnosis of medical issues always requires an expert and is also ***,developing some computer diagnosis techniques based on deep learning is *** cancer is the most frequently diagnosed cancer in females with a rapidly growing *** is estimated that patients with BrC will rise to 70%in the next 20 *** diagnosed at a later stage,the survival rate of patients with BrC is ***,early detection is essential,increasing the survival rate to 50%.A new framework for BrC classification is presented that utilises deep learning and feature *** significant steps of the presented framework include(i)hybrid contrast enhancement of acquired images,(ii)data augmentation to facilitate better learning of the Convolutional Neural Network(CNN)model,(iii)a pre‐trained ResNet‐101 model is utilised and modified according to selected dataset classes,(iv)deep transfer learning based model training for feature extraction,(v)the fusion of features using the proposed highly corrected function‐controlled canonical correlation analysis approach,and(vi)optimal feature selection using the modified Satin Bowerbird optimization controlled Newton Raphson algorithm that finally classified using 10 machine learning *** experiments of the proposed framework have been carried out using the most critical and publicly available dataset,such as CBISDDSM,and obtained the best accuracy of 94.5%along with improved computation *** comparison depicts that the presented method surpasses the current state‐ofthe‐art approache
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