Supervised Learning Experiments on Wisconsin Breast Cancer Dataset

A PyTorch implementation of MedSegDiff, a diffusion probabilistic model designed for medical image segmentation.

Code for: Exhaustive Exploitation of Nature-inspired Computation for Cancer Screening in an Ensemble Manner -- [IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB 24)]

Official repository of "Enhancing the Utility of Privacy-Preserving Cancer Classification using Synthetic Data"

Taşınabilir Cihazlarda Gerçek Zamanlı Kanser Tespiti ve Sınıflandırmasını Yapan Uygulama

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.

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

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

Prediction of Cancer Using Machine Learning Model

Adeno Carcinoma Cancer Classification

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

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

Breast Cancer Classification: Machine Learning-based Modeling with Streamlit

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

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.

Breast Cancer Detection using Machine Learning

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

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.

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.