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The cancer is a disease in which cells multiply uncontrollably and these cell crowd out normal cells. Breast cancer (BC) is one of the most common types of cancer disease.Breast cancer is the leading cause of death among women. Around 8% of women are diagnosed with Breast cancer (BC), after lung cancer it is the second popular cause of death in world. BC is characterized by the mutation of genes, constant pain, changes in size, color(redness), skin texture of breasts.Hematoxylin and eosin (H&E) stained breast tissue samples from biopsies are observed under microscopes for primary diagnosis of breast cancer. Several types of research have been done on early detection of breast cancer to start treatment and increase the chance of survival. Today, Machine Learning (ML) techniques are being broadly used in the breast cancer classification problem. They provide high classification accuracy and effective diagnostic capabilities.Deep-learning (DL) methods in artificial intelligence (AI) play a dominant role as high-performance classifiers in the detection of the disease using ultrasonic images. Given many new DL models have been being developed for this purpose, the objective of this study is to investigate the fine tuning of pretrained convolutional neural networks (CNNs) for the classification of BC using ultrasonic images. If fine-tuned pre-trained CNNs can provide equivalent or better classification results than other more sophisticated CNNs, then the deployment of AI-based tools for detecting BC using ultrasonic data can be more rapid and cost-effective.