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@article{153554,
        author = {kantubhuktha Bhargavi and Ravva Gurunadha },
        title = {Identification of Colon Cancer using Deep Learning Techniques},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {8},
        number = {8},
        pages = {178-184},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=153554},
        abstract = {The primary difficulty in distinguishing Colon Cancer Digital Pathology Photos is to differentiate benign from malignant disease. Colorectal cancer develops in the colon or the rectum. . Depending on where they originate, these cancers may be called colon cancer or rectal cancer, too. Since they have several characteristics in common, colon cancer and rectal cancer are frequently grouped together. Much of the current system relies on the characteristics of multiple frameworks that follow deep convolution neural networks (CNNs) used in the application of polyp detection in the current system, explaining the use of a deep learning model for polyp detection, while their system only achieved less than ideal accuracy (86%) and sensitivity (73%) and other methods. A standard support vector machine (SVM) classifier was trained to carry out polyp detection and classification of CNN features from a non-medical to a medical domain. Multi-scale fusion convolution neural network (MFF-CNN) feature based on shearlet transformation to classify histopathological picture of colon cancer Here we introduce the shearlet transformation approach fusion features and use VGG19 model that can provide better segmentation and classification accuracy for benign from malignant},
        keywords = {CNN,MF-CNN,ColonCancer,VGG19,Shearlet Transformation},
        month = {},
        }