Design and Development of a Novel Framework for Classification and Segmentation of Breast Tumors by Using Deep Learning Techniques

Abstract

Image segmentation is vital in image processing and computer vision, and it is also regarded as newlinea bottleneck in image processing technology development. Picture segmentation is the process newlineof dividing an image into a group of disjoint sections with uniform and homogeneous newlinecharacteristics. The convolutional neural networks (CNN) is a mainstream profound learning newlinebuild utilized in picture arrangement. This procedure has accomplished huge progressions in newlineenormous set picture arrangement challenges in later a long time. In this examination, we had newlineacquired more than 3000 excellent unique mammograms with endorsement from an newlineinstitutional survey board at the University of Kentucky. Various classifiers dependent on newlineCNNs were manufactured, and every classifier was assessed dependent on its exhibition newlinecomparative with truth esteems created by histology results from biopsy furthermore, two- year newlinenegative mammogram follow-up affirmed by master. In this research, a method for classifying newlinemammogram images is proposed that is based on training the convolutional neural networks newline(CNN). Furthermore, it shows the preliminary classification performance of using this CNN to newlineautomatically learn and categorise RGB-D images. For this four-class classification job, the newlinemethod of transfer learning known as fine tuning technique is proposed which involves reusing layers learnt on the ImageNet dataset to discover the optimal design. newlineBreast cancer is most popular and growing disease in the world. Breast cancer is mostly found newlinein the women. Breast cancer is a devastating disease, with high mortality rates around the world. newlineEarly detection is a way to control the breast cancer. Early detection of breast cancer through newlinescreening tests such as mammograms is an efficient way to maximize patients survival rate by newlinetreating the disease prematurely. Creating a model which will automatically classifying and newlinedetect breast cancer in early stage is challenging task.