Cervical Cancer Detection Using Deep Learning

Greetings everyone and thanks for joining this engaging project. In this project, we are making use of AI for a good reason and that is Cervical Cancer Detection using Deep Learning. Well, you're here if you are a geek, or a person more interested to know how deep-learning approaches are revolutionizing the healthcare sector. The mission of this project is to help you create a cervical cancer detection system using deep learning technology. Interesting right? Let's dive in!

Project Outcomes

✓Medical image classification using efficientNetB0 achieved over 97.7% accuracy.
✓Enhanced model generalization by employing more image preprocessing techniques such as image resizing
✓normalization
✓and augmentation.
✓Addressed image imbalance and enhanced the diversity of the dataset by implementing data augmentation methods.
✓Presented confusion matrices and generated accuracy/loss graphs to visualize how well the model has been trained.
✓Incorporated steps in the process of glaucoma diagnosis to lessen the burden on medical specialists and improve early disease detection.
✓The project contributes to the growth of AI in medical imaging.
✓Model Comparisons between which one is the best modeled multiple models (CNN
✓EfficientNet
✓Sequential).
✓Identified strengths and areas where performance needs to be improved with a visualized lens.
✓We used pre
✓trained weights to do training in less time and still get the performance.

Requirements:

  • →An understanding of Python programming and usage of Google Colab
  • →Basic knowledge about deep learning and medical images.
  • →Comfortable using frameworks like Tensorflow, Keras, Numpy, OpenCV, and Seaborn to handle data and build models and visualize data and performance of models
  • →The cervical cancer dataset consists of images labeled corresponding to the types.

Project Description

Project Overview

Cervical Cancer is extremely severe and to detect and classify it without any error is very crucial. In this project, you will learn to implement a working model for cervical cancer detection using deep learning. We'll show you how to do this on Google Colab from its computing capabilities. Also, you will learn how to work with an external dataset, process the data for the tasks, and include visualization for the assessment of the model's performance.

By the end of this project, you will have a complete application that can receive medical images and output an appropriate type of cervical cancer. Imagine having the ability for AI to assist a doctor in less than 5 minutes. Cervical cancer diagnosis using AI is the future that we are looking forward to today.


Prerequisites

Before we jump into the code, here's what you'll need:

  • An understanding of Python programming and usage of Google Colab

  • Basic knowledge about deep learning and medical images.

  • Comfortable using frameworks like Tensorflow, Keras, Numpy, OpenCV, and Seaborn to handle data and build models and visualize data and performance of models

  • The cervical cancer dataset consists of images labeled corresponding to the types.

Do not stress if you are new to the deep learning world. This project breaks down complex deep-learning tasks in a simple way.


Approach

The process is aimed at developing an accurate Cancer detection CNN model in the following order:

  • Data Preprocessing: Here we have to prepare our dataset for feeding the model. We applied resizing, normalizing techniques.

  • Model design: Propose the architecture of CNN including the layers. Which would be able to extract relevant fields from the given images.

  • Training & Evaluation: Train the model and check how well the model works in real-time.


Data Collection

Data is the backbone of any such work. While carrying out cervical cancer, we used a cervical cancer dataset from Kaggle consisting of a variety of countries' cervical cell images. The data was then used to create the main training set and the validation set for training and testing purposes respectively.

Data Preparation

The images were also preprocessed by using OpenCV to re-sized the images to a standard size of 128 x 128 pixels.

Data Preparation Workflow

  • Resize Images: Adjustment of image size to a particular standard.

  • Normalization: Scale the pixel values between 0 and 1.

Cervical Cancer Detection Using Deep Learning

Our Cervical Cancer Analysis project leverages the power of EfficientNetB0 to accurately classify different types of cervical cells. This initiative aims to enhance early cancer detection and improve patient outcomes through advanced AI technology.

$20$15.0025% off