• Mr Manoj Patel

The big bowel is where colorectal cancer frequently evolved. It is a widespread disease that affects millions of people worldwide every year. Early diagnosis can prevent many people from suffering from this condition. However, manually controlling and assessing various medical illustrations is difficult and takes a while. Artificial intelligence techniques can thus be utilized to support medical practitioners and carry out operations quickly and efficiently in the detection of colorectal cancer. In this work, a median filter is used to remove noise from an input colorectal cancer image. The filtered image is then segmented using colorbased segmentation with K-means clustering. A success (accuracy) percentage of 99.93% was obtained using the Novel model. It has been demonstrated that the suggested approach can lead to the early detection of Colorectal cancer.

Classification, Image Processing, GoogLeNet model, Colorectal Cancer, Deep Learning

"Finding and Cataloging of Colorectal Pictures Founded on Fresh GoogLeNet Typical", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 7, page no.231-236, July-2018, Available :http://www.jetir.org/papers/JETIR1807A33.pdf

"Finding and Cataloging of Colorectal Pictures Founded on Fresh GoogLeNet Typical", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 7, page no. pp231-236, July-2018, Available at : http://www.jetir.org/papers/JETIR1807A33.pdf