Cervical cancer is the second most common type of cancer that is found in the women worldwide. Generally, cancer caused due to irregular growth of cells in a particular area that or have the potential to spread to the other parts of the body as well. Identification of a cervical cancer test is an examination of the tissue taken from a particular region, which might contain cancerous cells through biopsy, is exceptionally challenging because these types of cells does not offer unusual color or texture variants from the standard cells. To identify the abnormalities in human cell the high-level digital image processing technologies are already present in the market which very costly concerning the money. Therefore, we are proposing the model which going to classify whether a female patient has cervical cancer or not. We are using various attributes from real-life and performing a feature selection algorithm Recursive Feature Elimination (RFE). Afterward, making classification models using three machine-learning algorithms like K-Nearest Neighbor (KNN), Random Forest and Multilayer Perceptron (MLP), MLP is a type of the Artificial Neural Network (ANN) algorithm whereas KNN and Random Forest is a supervised type of algorithm.

Attempt at the classification of cervix types for cancer treatment, based on the Kaggle Competition in this link: https://www.kaggle.com/c/intel-mobileodt-cervical-cancer-screening

Cervical cancer type classification using cnn

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Automated cervix type classification and cervical cancer diagnosis GUI

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Classification using Machine Learning Algorithms: Prediction of Cervical Cancer and White Wine Type

Classification of cervix types for cervical cancer screening. The classification is made from images and uses neural networks.

Classification of cervical cancer types. Includes training and finetuning a pretrained Convolutional Neural Network (CNN), and a streamlit web app for testing and inference.

Engineered a robust deep learning pipeline to classify Pap smear images into five cell types, aiding early cervical cancer diagnosis. – Integrated advanced CNN architectures (ResNet50, DenseNet121, EfficientNetB5) with RNN, LSTM, and XGBoost to enhance feature learning and classification accuracy.

It's a competition in kaggle. Dataset could be fetched at https://www.kaggle.com/cpruce/cervix-image-segmentation/data In this competition, you will develop algorithms to correctly classify cervix types based on cervical images. These different types of cervix in the data set are all considered normal (not cancerous). Identifying the transformation zones is not an easy task for the healthcare providers, therefore, an algorithm-aided decision will significantly improve the quality and efficiency of cervical cancer screening for these patients. The goal is to do a classification for three types transformation zones and try to predict the type for each transformation zone according to the image. The dataset is really large, almost around 80G, so I build a server in Google Cloud platform to handle this. Read the images by cv2 library, and split the data into two parts, train set and test set. Extract the gradient features of images by watershed algorithm and Stochastic gradient descent. Create VGG model to train the data, the final prediction accuracy reaches 60%.