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@article{148149,
        author = {Saurabh Waikule and Prachi Shingvi and Shashank Thigale and M. R. Mahajan},
        title = {Classification of breast cancer based on histological images using CNN},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {5},
        number = {12},
        pages = {604-608},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=148149},
        abstract = {Breast cancer is one of the most significant reasons for death among women. Early diagnosis significantly increase the chance of correct treatment and survival but this process is tedious and often leads to disagreement between pathologists. Computer based analysis showed potential for improving diagnostics accuracy. Many research has been done on the detection and classification of breast cancer using various image processing and classification techniques. The performance of most conventional classification system relies on feature extraction and appropriate data representation. On the other hand deep learning can organize discriminative information from data. Using CNN we propose a method for classification of histology images into benign and malignant and also subclasses. For 2 class classification task, we report 88% accuracy and for 4 class classification task, we report 85% to 89% accuracy.},
        keywords = {Breast cancer, Classification, Convolutional Neural Network (CNN), Deep learning, Histopathological image.},
        month = {},
        }