A collection of small-sample, high-dimensional microarray data sets to assess machine-learning algorithms and models.

A multi-centre polyp detection and segmentation dataset for generalisability assessment https://www.nature.com/articles/s41597-023-01981-y

Image classifier for colon cancer detection from colonoscopies.

Image classification on lung and colon cancer histopathological images through Capsule Networks or CapsNets.

Kvasir-SEG: A Segmented Polyp Dataset

GitHub repository for Medico automatic polyp segmentation challenge

KBSMC colon cancer grading dataset repository

Detection of Colon Cancer Cell and its types using a semi-supervised approach with deep learning

Codes for parameter estimation and sensitivity analysis of QSP models for colon cancer. This is a part of the National Cancer Institute funded project titled "Data-driven QSP software for personalized colon cancer treatment" Achyuth Manoj, Susanth Kakarla, Suvra Pal and Souvik Roy.

Repo which includes the medical data sets used in a feature selection paper proposed by OASYS group

Find patients who have concerning tests but no timely follow-up

Exploring the Supervised Learning Models to Automatically Diagnose Colon Cancer Patients based on their SNP Profiles

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.

KBSMC_colon_tma_cancer_grading_1024_dataset

TRAL pipeline for tandem repeat detection in proteins. Specifically in such which are related to colorectal cancer.

Rasa Gastroenterologist AI Chatbot to help doctor detect patients colon cancer lesions using Unity, Darknet Yolo, Keras CNN

colorectal cancer

Colorectal cancer (CRC) is the second most dangerous type of cancer in terms of causing deaths in patients and third most common type of cancer found in people in terms of incidence. CRC can be further categorized based on its molecular subtypes. Each subtype displays different features. Thus, identifying molecular subtypes of CRC and treating …

The goal of this analysis is to explore the machine learning-based automatic diagnosis of colorectal patients based on the single nucleotide polymorphisms (SNP). Such a computational approach may be used complementary to other diagnosis tools, such as, biopsy, CT scan, and MRI. Moreover, it may be used as a low-cost screening for colorectal cancers