This project presents the better Computer Aided Diagnosing (CAD) system for automatic detection of lung cancer. The initial process is lung region detection by applying basic image processing techniques such as Bit-Plane Slicing, Erosion, Median Filter, Dilation, Outlining, Lung Border Extraction and Flood-Fill algorithms to the CT scan images. After the lung region is detected, the segmentation is carried out with the help of Mean Shift clustering algorithm. With these, the features are extracted and the diagnosis rules are generated. These rules are then used for learning with the help of Random Forest. The experimentation is performed with 15, 000 images obtained from the kaggle contest. The experimental result shows that the proposed CAD system can able to tell the posterior probability of lung cancer for a patient based on the detection algorithm. Also the usage of Random Forest will increase the accuracy of detecting the cancer nodules.

Early detecting of lung cancer using the Luna data set with LIDC IDRI annotations using two models nodule classification"Googlent model" and the malignancy classification "Lenet model". This was for kaggle's Data science bowl 2017.

Develop the model for predicting the breast cancer with the help of image dataset from kaggle and it's implemented with the help of CNN

Project for Kaggle competition on spotting cancer by NLP - coworking with Rafi Zonenashvili

Code for The UW Madison Kaggle Competition - Segmenting intestine and stomach for Cancer patients with TensorFlow

Medical App Based on Deep Learning with Medical Watch Based on IOT [ Dr Care ] •The main objective of this project is to Health Care. - Doctor Care is an application to help patients in their healthy condition as it benefits them in more than one way, such as communicating with doctors in an easy way and other advantages. - The specific objective of this project is :- • Doctor Care able to examine images such as (Brain Tumor – Chest x-ray - Skin Cancer, Heartbeat, Retinal OCT). • Medical Watch is the part of hardware can measure (oxygen ratio - heartbeat - temperature), the application read these measurement and sends an alarm when there's a sudden malfunction in the health condition of the elderly based on IOT. • Doctor Care will provide us with Chat bot to detect if a person has a certain disease by talking with the chat bot, or also know information about a specific disease or treatment for a disease • Doctor Care will provide us with making posts about diseases where the patient talks about what hurts him and someone gives advice to him, interaction with posts. • Doctor Care will give us information about where the hospital to us in the map section and the application displays the expected time to reach the hospital if a particular mode of transport is used or on foot. • The patient can contact to the doctor online. • Doctor Care allows you to browse medical news when online or offline. • Doctor Care allows you to log in and out as a (patient or Doctor) • Doctor Care also provides us with setting alarms to follow up on taking medications and this helps in treating chronic conditions such as diabetes, heart disease, etc. - Used Tools :- • Android. • Arduino. • Deep Learning. • Firebase. • Fast API. • Adobe XD. • Kaggle. • Azure. • Heroku. .

This project is based on Gene expression dataset from Kaggle. Here Molecular Classification of Cancer by Gene Expression monitoring Dataset is done. This dataset comes from a proof-of-concept study published in 1999 by Golub et al. It showed how new cases of cancer could be classified by gene expression monitoring (via DNA microarray) and thereby provided a general approach for identifying new cancer classes and assigning tumors to known classes. These data were used to classify patients with acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL).

Experiments with processing the lung CT scans that are publicly available in the kaggle competition Data Science Bowl 2017. Evaluating different deep neural networks for training a model that helps early cancer detection.

In this brief project, i conduct an extented analysis on breast cancer data (data provided by https://www.kaggle.com/uciml/breast-cancer-wisconsin-data). I analyze the correlation between the features to extract the most valuable. I train 10 models with the initial features and then train the same models with the extracted features to show the effectivesness of the feature extraction methods used.

In this project, I experimented with various machine learning algorithms on a dataset from Breast Cancer Wisconsin (Diagnostic) Data available on UCI repository and kaggle. This dataset provides various features along with a target variable of diagnosis. I learned how different machine learning techniques can be applied to find the patterns in the data and classify the tumor as benign or malignant. Through this project, I gained a lot of experience in data wrangling, feature engineering and machine learning algorithms, and compare the results obtained on different ML models based on accuracy and ROC-AUC.

This is a repository for the ISIC 2024 - Skin Cancer Detection with 3D-TBP Kaggle Competion

Kaggle notebook exploring breast cancer data with visualizations, survival analysis, and statistical tests to uncover insights and patterns.

Creating a logistic regression algorithm without using a library and making cancer classification with this algorithm model (Kaggle Explained)

The project employs deep CNNs for feature extraction from Kaggle skin cancer images, enhancing classification accuracy. Integrated with traditional ML algorithms, the system is deployed in Django, providing a user-friendly web interface for real-time skin cancer predictions and early detection.

A ML project utilizing CNN for breast cancer detection through image processing. Achieved an accuracy of 97% using a dataset from Kaggle, where images were manually structured and processed for feature extraction with CNN, followed by classification using SVM.

The goal is to predict pneumonia - related lung opacities along with their bounding boxes in the chest X-ray images. The objective is to detect whether to classify the lungs which has pneumonia and discard other kinds of discard other types of opacities like the ones caused by fluid, bacteria, lung cancer etc. There are various algorithms that can be applied but we went ahead with YOLO which proves to be the best. We took most of the help from https://www.kaggle.com/c/rsna-pneumonia-detection-challenge. The dataset is also been downloaded from the kaggle website.

Polycystic Ovary Syndrome (PCOS) is a disease that affects the female reproductive, endocrine, and possibly immune systems. The exact etiology of the disease is still unkown but PCOS can lead to diabetes, infertility and even cancer. The most common symptom is having multiple fluid filled sacs on your ovaries. This project uses a Kaggle dataset of Indian women with and without PCOS to identify the prominence of other indicators of the disease.

Description of the Project: + The "Breast Cancer Dataset" is used in this project. It has df.shape=(569, 31) which means 569 rows and 32 columns. + The link of the datset used in this project is -https://www.kaggle.com/uciml/breast-cancer-wisconsin-data + I am importing the important python packages- skelarn, pandas, numpy, seaborn and matplotlib to complete the project. + The machine learning models such as Logistic Regression, Decision Tree, Random Forest, XGBoost, AdaBoost and Gradient Boosting classifier have been used. + The performance of the machine learnig models have been tested on the basis of accuracy score, confusion matrix, classification report, f1 score and roc auc score. + I had tuned hyperparameters to improve the perforamnce for XGBoost model + The good visualization is also important along with accuracy score in model building. The performance of the model have been visualized in this project. Problem statement: The full form of XGBoost is eXtreme Gradient Boosting, also called winner for several kaggle competetion machine learning model. Most of the literatues of Machine Learning found in google has described this model as having best accuracy, efficient and feasibility. It is a decision-tree-based ensemble ML algorithm based on gradient boosting framework. It is considered that XGBoost provides a convenient way of cross-validation. Cross-validation technique is applied to test the model's overfitting during the training phase. If the model gives good accuracy in training dataset but the model works very poor in testing unseen dataset then it is called overfitting or a model of low bias and high variance. I have to calculate the model training and testing errors with different learning rates.As we know that the best technique to choose the learning rate value is between 0 and 1. I will be going to start the test by putting the learning rate as 0.01. It would easy to see the results through good visualization. I am also going to visualize the training and testing errors and accuracies by making a graph. Finally, I will tune the hyperparameters which helps us predict the testing datasets i.e. x_test.

Utilize K-Nearest Neighbors (K-NN) for precise benign and malignant cancer cell classification in our Cancer Data Classification project.

Breast Cancer Prediction project with Kaggle data

Logistic Regression for Cancer Data Classification: Achieve 96.50% accuracy in benign vs. malignant cell classification.

Making cancer classification with knn module (Kaggle Expression)

A cancer detecting module make with Kaggle notebook.

Experiments with kaggle Challenge: ISIC 2024 - Skin Cancer Detection with 3D-TBP

repo for training and experiments with skin-cancer dataset from kaggle

Comparisson between SVMs, with linear, poly, and rbf kernel, for dataset https://www.kaggle.com/uciml/breast-cancer-wisconsin-data

Predicted whether the cancer is malignant or benign using logistic regression with an accuracy of 94%.The dataset is downloaded from Kaggle

This project employs AI for early skin cancer detection, using deep learning and image analysis. It utilizes the Skin Cancer 9 Classes ISIC and Skin Cancer MNIST HAM10000 datasets from Kaggle. With diverse data, it accurately classifies lesions, aiding in timely diagnoses for better patient outcomes.

This project detects breast cancer from mammograms using deep learning, including preprocessing, training, and evaluation. It uses the RSNA Screening Mammography Dataset (\~55k DICOM images from Kaggle) with cancer labels and sometimes lesion boxes, enabling automated screening and model benchmarking.

This project detects breast cancer with 97% accuracy using a Kaggle dataset. It preprocesses data, performs EDA, and trains models like Logistic Regression and SVM. Built with Python and scikit-learn, it aids in early cancer diagnosis and highlights the role of machine learning in healthcare.