Colorectal Cancer Detection by using Deep Learning

Project Code :TCMAPY1048

Objective

The objective is to develop a Convolutional Neural Network (CNN) based deep learning model for accurate and early detection of colorectal cancer from medical images, enhancing diagnostic precision, and potentially improving patient outcomes through timely intervention.

Abstract

Colorectal cancer is a significant global health concern, accounting for a substantial portion of cancer-related deaths worldwide. Early detection and timely intervention are pivotal in improving patient outcomes. This paper presents an innovative approach for the early detection of colorectal cancer through the application of deep learning techniques.

We leverage a large dataset of medical images, including colonoscopy and histopathology slides, to develop a robust and accurate deep learning model for colorectal cancer detection. Convolutional Neural Networks (CNNs) and advanced neural architectures are employed to automatically extract meaningful features from these images, enabling the discrimination between cancerous and non-cancerous tissues.

Keywords: colorectal dataset, deep learning algorithms etc...

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.

Block Diagram

Specifications

H/W CONFIGURATION:

Processor - I3/Intel Processor

Hard Disk - 160GB

Key Board - Standard Windows Keyboard

Mouse - Two or Three Button Mouse

Monitor - SVGA

RAM - 8GB


S/W CONFIGURATION:

Operating System :  Windows 7/8/10

Server side Script        :  HTML, CSS, Bootstrap & JS

Programming Language   :  Python

Libraries      :  Flask, Pandas, Mysql connector, Os, Smtplib, Numpy

IDE/Workbench      :  PyCharm

Technology     :  Python 3.6+

Server Deployment     :  Xampp Server


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