cancer-classification

scMalignantFinder is a Python package specially designed for analyzing cancer single-cell RNA-seq datasets to distinguish malignant cells from their normal counterparts.

A PyTorch implementation of MedSegDiff, a diffusion probabilistic model designed for medical image segmentation.

Multilayer recursive feature elimination based on embedded genetic algorithm for cancer classification

This repository contains a deep learning-based cancer type prediction system using a trained convolutional neural network (CNN). The model is deployed using Streamlit, allowing users to upload medical images and receive predictions with a probability distribution displayed in a pie chart.

Predict which cell is cancerous with 96% accuracy using SVM machine learning algorithm.

Classification of HAM10000 dataset using Pytorch and densenet

(MIDL 2023) Code for "Reverse Engineering Breast MRIs: Predicting Acquisition Parameters Directly from Images"

BSc thesis: "Convolutional Neural Networks and their Application in Cancer Diagnosis based on RNA-Sequencing"

Taşınabilir Cihazlarda Gerçek Zamanlı Kanser Tespiti ve Sınıflandırmasını Yapan Uygulama

Malignancy classification using simple deep learning method in LIDC-IDRI dataset.

Code for: Exhaustive Exploitation of Nature-inspired Computation for Cancer Screening in an Ensemble Manner -- [IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB 24)]

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

In this part, we developed an interface for Skin Cancer Classification using the Tkinter library in Python.

Glioblasted is a machine learning model to assist in the detection of glioblastoma multiforme, a high-grade, aggressive form of central nervous system cancer.

Skin Cancer Classification

End-to-end multi-omics cancer subtype classifier XGBoost, pathway-aware deep fusion network (mRNA/miRNA/methylation/CNV), KEGG attention, SHAP/IG explainability, and a Streamlit demo. TCGA BRCA & COAD. PyTorch.

Classifying lung cancer subtypes (LUAD vs LUSC) from TCGA histopathology slides using InceptionV3 on NYU HPC. Built on top of Barlow Twins SSL embeddings and the HPL framework.

Computationally efficient multi-scale foundation model for computational pathology. 71M parameters, runs end-to-end on a single consumer GPU.

Official repository of "Enhancing the Utility of Privacy-Preserving Cancer Classification using Synthetic Data"