A flask website for cancer detection and diagnosis using machine learning

cancerSCOPE, a python library for cancer diagnosis

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.

A comprehensive classification tool based on pure transcriptomics for precision medicine

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

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.

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

In this problem statement, a sequence of genetic mutations and clinical evidences, i.e. descriptive texts as recorded by domain experts are used to classify the mutations to conclusive categories, to be used for diagnosis of the patient.

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.

Cancer diagnosis (using supervised machine learning and AI to determine whether tumor is malignant or benign)

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

Developed a ML model to predict cancer diagnosis using a Kaggle dataset, as part of ECS 171 (Machine Learning) at University of California, Davis, under the instruction of Dr. Setareh Rafatirad. Key contributions: 1) Implemented Regression, KNN, and Random Forest models. 2) Applied EDA analysis and feature selection

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