cancer-diagnosis

A flask website for cancer detection and diagnosis using machine learning

cancerSCOPE, a python library for cancer diagnosis

End-to-end cancer diagnosis using Machine learning and Flask for a web interface.

Classify the given genetic variations/mutations based on evidence from text-based clinical literature.

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

This repository contains the codes for reproducing the results obtained by out DeepHistoPathology model for Ivasive Ductal Carcinoma open Dataset cancer detection

Performing Cancer Diagnosis via an Isoform Level Expression Ranking-based LSTM Model

Problem Statement : Classify the given genetic variations/mutations based on evidence from text-based clinical literature.

This notebook provides source code of my work for my Master's Thesis.

A comprehensive classification tool based on pure transcriptomics for precision medicine

End-to-end multimodal AI platform for cancer classification, stage prediction, risk factor analysis, and biomarker discovery, integrating histopathology images, genomic mutations, transcriptomic expression profiles, and epigenomic methylation data to deliver accurate predictions, interpretable insights, and advanced multi-omics cancer intelligence.

Project focuses on diagnosing cancer through image analysis. It utilizes machine learning models and techniques to analyze medical images and classify cancerous cells or tumors. It aims to improve cancer diagnosis accuracy and assist healthcare professionals.

Breast cancer is one of the most common types of cancer among women, with early detection being crucial for effective treatment and survival. Project Includes Source Code, PPT, Synopsis, Report, Documents, Base Research Paper & Video tutorials

This project consists of the analysis of Breast Cancer dataset and exploration of different machine learning models for predictions of diagnosis of tumors based on tumor cells characteristics.

AI Powered Diagnostic Assistant

End-to-end tumor-normal WGS pipeline using BWA, GATK Mutect2, SnpEff and cancer-gene prioritization to identify candidate breast cancer driver mutations.

AI-powered app using logistic regression to predict breast cancer diagnosis from tumor measurements with high accuracy 97.3%.

Code and experiments for "Non-convex SVM for cancer diagnosis based on morphologic features of tumor microenvironment"