hemalathakkk Goto Github PK

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age-classification-using-transfer-learning

ai4goodlab_combatcervicalcancer

cervical-cancer

Cervical cancer occurrences

cervical-cancer-classification

The project work includes implementation of a Springer paper which uses transfer learning on pap smear images to detect and classify cancer into seven different classes. It uses pre-trained CNN models and classifying algorithms to obtain results.

cervical-cancer-classification-sipakmed

cervical-cancer-prediction

In this data set, We have to predict the patients who are most likely to suffer from cervical cancer using Machine Learning algorithms for Classifications, Visualizations and Analysis.

cervical-cancer-risk-detection-project

A computer vision project:Alibaba "Tianchi" competition --- cervical cancer risk detection

cervical-cancer-risk-factor-analysis

Analysis of various risk factors associated with cervical cancer

cervical_cancer_cnn

Kaggle Challenge "ODT/Intel Cervical Cancer" contribution as Stanford CS231N class project by Stephen Pfohl, Oskar Triebe and Ben Marafino

cervical_cancer_kaggle

Implementation of a classification algorithm which accurately identifies cervix type based on images for Kaggle challenge using Keras

cervicalcancerprediction

Comparative study of Multi-Label Classification, Ensemble Based Learning and Artificial Neural Network for Cervical Cancer Prediction

cgan

clothes_gan

deep-learning-basics-tf

Deep Learning basics with Tensorflow and Keras. It includes implementing deep neural networks, feed-forward neural networks, convolutional neural networks, and a traffic sign detection system, using GTSDB and CIFAR dataset.

dl-projects

Handwritten digit recognizer with an accuracy of 97.72 %

facebook-detr

ga

gan

gan1

ganburger

gann

hems

histogram-equilization-and-matching-of-an-image

implement-and_or_logic_gate_using_perceptron

kaggle-cervical-cancer-screening-classification

Solution and summary for Intel & MobileODT Cervical Cancer Screening (3-class classification)

kaggle-mobileodt

Code for Intel & MobileODT Cervical Cancer Screening competition on Kaggle https://www.kaggle.com/c/intel-mobileodt-cervical-cancer-screening

machine-learning-based-classification-of-cervical-cancer-using-k-nearest-neighbor-random-forest-and

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.