Because ultrasonography is noninvasive, radiation-free, and reasonably priced, it is a crucial regular examination for the identification of breast cancer. Still Breast cancer diagnosis accuracy is still not very high, because of its intrinsic shortcomings. Next, an accurate diagnosis using an image from a breast ultrasound (BUS) would be important for numerous computer - aided diagnostic learning. There are techniques that can cause breast cancer lesion classification and diagnosis. But the majority of them need to first define a region of interest (ROI), and then categorize the ROI's internal lesion. This project is mainly focused on classification of breast cancer classification by the implementation of the hybrid algorithm of XGBoost and linear regression model for the classification of classes. For achieving this loading and preprocessing the dataset and the use the deep learning concept for the feature extraction for the classes and then save it as a features file and then apply the hybrid algorithm of XGBoost and then linear regression for the classification purpose and then find the accuracy, precision and recall of the model for the evaluation. This mixed up of hybrid and CNN get an accuracy of almost 97% for the classification.

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