• Prem Chand Yadava
• Kamraj Kushawaha
• Nimisha Yadav
• Jyoti Prashant Singh
• Vishal Yadav

The big bowel is where colorectal cancer frequently evolved. It is a widespread disease that affects millions of people worldwide every year. Like other types of cancer, initial and precise detection is essential in treating Colorectal cancer. Early diagnosis can prevent many people from suffering from this condition. Clinical imaging methods are commonly accessed to locate, track and treat Colorectal cancer at an early stage. However, manually controlling and assessing various medical illustrations is difficult and takes a while. In addition, human errors from common methods of interpreting data during this process can lead to misdiagnosis. 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 color-based segmentation with K-means clustering. Finally, colon cancer is classified using various deep learning approaches, such as MA_ColonNET and GoogLeNet. The comparative assessment showed that Novel GoogLeNet is the best classifier for colon cancer classification. A Novel GoogLeNet model is built into this work to identify the Colorectal cancer images dataset. For classification, a 144-layer model was employed in the GoogLeNet model. 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. The healing process will be more successful as a result.

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

"Detection and Classification Colorectal Histopathological Images Based on Novel GoogLeNet Model", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 9, page no.b566-b580, September-2023, Available :http://www.jetir.org/papers/JETIR2309160.pdf

"Detection and Classification Colorectal Histopathological Images Based on Novel GoogLeNet Model", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 9, page no. ppb566-b580, September-2023, Available at : http://www.jetir.org/papers/JETIR2309160.pdf