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.

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

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.

Supervised Learning Experiments on Wisconsin Breast Cancer Dataset

This work aims to analyze data corresponding to patients diagnosed with breast cancer, apply data mining to predict disease recurrence, and compare the performance of machine learning techniques in breast cancer relapse classification.

A comprehensive Jupyter notebook project that uses Support Vector Machines (SVM) for the classification of breast tumors into malignant or benign categories. The notebook includes data exploration, visualization, model training, and evaluation, providing insights into breast cancer diagnosis using machine learning.

Breast Cancer Classification: Machine Learning-based Modeling with Streamlit

Building a deep learning model to make colorectal cancer histology classification

The goal of this project is to classify cancerous images (IDC : invasive ductal carcinoma) vs non-IDC images.

Cancer Classification Using Gene Expression Data with the use of different Regression ML based models.

Breast Cancer Detection using Machine Learning

Developed a fine-tuned EfficientNetB0 model which is a pre-trained Convolutional Neural Network (CNN) model to train using lungs and colon cancer dataset and classify if the unseen image belonged to benign, adenocarcinoma or squamous cell carcinoma cancer type.

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

Breast Cancer Prediction: Machine Learning-based Diagnosis with Streamlit

Skin Cancer Classification

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