Classification of Benign and Malignant Tumor
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Updated
Aug 29, 2024 - Jupyter Notebook
Classification of Benign and Malignant Tumor
Python toolkit for parsing, processing, and analysis of Illumina methylation array IDAT files
Code for paper: DGR-MIL: Exploring Diverse Global Representation in Multiple Instance Learning for Whole Slide Image Classification [ECCV 2024]
The MAMA-MIA Dataset: A Multi-Center Breast Cancer DCE-MRI Public Dataset with Expert Segmentations
As part of the Thesis project, an Artificial Intelligence model was trained to classify breast tumors from ultrasound images as benign or malignant. Additionally, a user-friendly web application was created with Python for the backend and ReactJS for the frontend.
An attempt to build accurate models for detecting various brain diseases
scMalignantFinder is a Python package specially designed for analyzing cancer single-cell RNA-seq datasets to distinguish malignant cells from their normal counterparts.
R package that automatically classifies the cells in the scRNA data by segregating non-malignant cells of tumor microenviroment from the malignant cells. It also infers the copy number profile of malignant cells, identifies subclonal structures and analyses the specific and shared alterations of each subpopulation.
The Brain Tumor MRI Dataset from Kaggle is employed for automated brain tumor detection and classification research. Investigated methods include using pre-trained models (VGG16, ResNet50, and ViT). 🧠🔍
Brain tumor classification based on MGMT methylation status present on the tumor cell.
Tumor Identification Based on Machine Learning Analysis of Gene Expression (2024)
Use Logistic Regression and Supported Vector Machines (both in scikit-learn) to classify whether a tumor is malignant or benign
The goal of this project is to develop a tumor detection model utilizing logistic regression. This model aims to analyze medical imaging data and predict the likelihood of the presence of tumors.
Topological data analysis (TDA) in medical image analysis
Using Naive Bayes for tumor classification in medical images. Great for healthcare & data science. Python & scikit-learn powered
A Real-Time Multi Class Brain Tumor Classifier to Classify Brain Tumors using ConvNets (CNNs) and ANN as an asset of Deep Learning and to examine the position of the tumor.
Principal Component Analysis and Support Vector Machine for tumor classification in patients from the original Wisconsin breast cancer database.
Tumor Prediction Web App
In this we trained a model to detect if there is a tumor in the brain image given to the model. Meaning a model for binary class with an accuracy of above 90 for same and cross validation.
[a.a. 22/23] G. Antonucci, N. Pagliara
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