A deep learning project for cervical cancer detection, classifying cervical cell images into 5 classes. It uses pre-processing techniques like SLIC super pixel segmentation and Canny edge detection, followed by fine-tuning pre-trained CNN models like ResNet50, VGG16, InceptionV3, EfficientNetB0-B7 and MobileNetV2-V3 to compare model performance.

This repository contains a Python module for predicting cervical cancer using machine learning (ML) models, specifically leveraging the power of transformer architectures. The goal of this project is to provide a reliable tool for early detection of cervical cancer, aiding healthcare professionals in diagnosis and treatment planning.

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A deep learning project for cervical cancer detection, classifying cervical cell images into 5 classes. It uses pre-processing techniques like SLIC super pixel segmentation and Canny edge detection, followed by fine-tuning pre-trained CNN models like ResNet50, VGG16, InceptionV3, EfficientNetB0-B7 and MobileNetV2-V3 to compare model performance.

Deep learning-based cervical cancer detection from medical images using Python and CNNs

Risk Detection of Cervical Cancer using XGBoost Algorithm[Extreme Gradient Boosting] I have used XGBoost model to predict cancer in patients from a dataset from "Hospital universitario de caracas" which is situtated in Caracas, Venezuela. The dataset contains all the information and habits of 858 patients. On a worldwide Scale about 311,000 women have died from this disease. One of the key factors which leads to the risk of this anomaly is HPV(Human Papilloma Virus). In this project I have used various python libraries 1.Numpy 2.Pandas 3.Seaborn 4.Matplotlib Then, I did some exploratory data analysis on my dataset and then was able to do some data visualization using seaborn and matplotlib which leads me for the preparation of the data and finally after all this I used XGBoost algorithm to train and evaluate my model.