A flask website for cancer detection and diagnosis using machine learning

cancerSCOPE, a python library for cancer diagnosis

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

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

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

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

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.

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

Utilizing SVM for breast cancer classification, this project compares model performance before and after hyperparameter tuning using GridSearchCV. Evaluation metrics like classification report showcase the effectiveness of the optimized model.

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

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"

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.

Machine Learning - Multiclass Classification