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@article{151093,
        author = {Kokulavanee S and Kosuri Naga Preethi  and Selvi M},
        title = {Breast cancer detection using machine learning },
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {7},
        number = {11},
        pages = {639-644},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=151093},
        abstract = {Breast cancer is quite possibly the most risky sicknesses and the second biggest reason for female disease demise. Breast cancer grows as threatening, damaging bumps grow on the breast cells. Individual testing and periodic health reviews aid in early diagnosis and, as a result, reduce overall endurance risks. Breast cancer characterization is a scientific procedure that gives experts and researchers a place to go specific examination. In disease knowledge characterization, neural networks have recently become a common subject of discussion well-known apparatus. In this article, a numerically proposed Deep Learning aided Efficient Ada boost Algorithm for breast disease exploration using cutting-edge computational methods is presented. Notwithstanding customary PC vision draws near, tumour characterization strategies utilizing moves are as a rule effectively created using profound convolutional neural network (CNN's). This investigation starts with a look at the CNNbased exchange to see how to describe breast masses for different analytic, predictive, or prognostic reasons, as well as in a few imaging modalities like MRI, ultrasound, computerised breast tomosynthesis, and mammography. The profound learning system employs a few convolutional layers, LSTM, and Max-pooling layers. For a fully related layer and a fragile max layer, the structure and error assessment that has been remembered. The aim of this paper is to combine these AI methods with techniques for selecting highlights and splitting them by evaluating their yield using grouping and division procedures to find the best technique. When compared to other current systems, the trial findings indicate that the 97.2 percent precision stage, sensitivity of 98.3 percent, and accuracy of 96.5 percent.},
        keywords = {},
        month = {},
        }