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

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

Classification of HAM10000 dataset using Pytorch and densenet

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

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

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

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

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

Breast Cancer Prediction: Machine Learning-based Diagnosis with Streamlit

Criação de Rede Neural Multilayer Perceptron capaz de classificar corretamente casos de câncer de mama

CT Scan Chest Cancer Classification using Deep learning, Transformers, mlflow, DVC, AWS

Skin Cancer Classification

Bioinformatics project analyzing cancer metabolism using computational modeling and analysis. The project was awarded the GIDI-UP: Summer Research Award and includes data, models, and scripts.

A machine learning tool that uses gene expression data to classify cancer types and predict mortality rates.

Built a classifier using Logistic Regression model to classify different species of flowers

Colorectal Disease Classification Using ResNet and ResNeXt

This is a Bio Informatics project for the classification of types of Leukemia Cancer i.e., ALL & AML based on gene expression data. An accuracy of 0.94 has been achieved by using Support Vector Machine(SVM). The dataset has been collected from 'Kaggle' where gene descriptions are given as the features.

Learning Vector Quantization ( or LVQ ) is a type of Artificial Neural Network which also inspired by biological models of neural systems. It is based on a prototype supervised learning classification algorithm and trained its network through a competitive learning algorithm similar to Self Organizing Map.

Building a deep learning model to make colorectal cancer histology classification