medicalimaging

Simplifying deep learning for medical imaging

ROS 2 pipeline that receives DICOM files over the network and translates them into standard ROS 2 topics

Repository for Master thesis project investigating classification of 3D chest CT scans using Vision Transformer.

REMEX Lab's home page.

This script reads DICOM files in a source directory or in a list of source directories and searches for the patients in the given patients' list creates a DICOM DataBase in the destination directory, copies the files, and creates a DicomDataBase.csv file and a summary.txt file.

Brain Tumor Detection using CNN & FastAPI.

A website to distinguish whether a skin lesion is malignant or benign: cancerous or not. The server is currently offline.

Project Overview:- The objective of this hackathon challenge is to develop a robust and efficient algorithm or AI model capable of accurately segmenting the hypodense region from Brain Non-Contrast Computed Tomography (NCCT) images. The primary goal is to automate and streamline the identification of early ischemic changes in acute stroke patients.

Empowering healthcare with AI, LeukoVision automates the detection of leukemia cells in blood smear images. Revolutionizing leukemia care with speed and accuracy.

Brain tumor detection app using pytorch for training and streamlit for GUI

Repository containing files associated with ENIGMA diffusion weighted image preprocessing protocol and instructional video series. Designed by Ryan Cali for the 2022 ReproNim Fellowship Project and the Imaging Genetics Center at the University of Southern California. Questions? Reach out to the author: rcali@loni.usc.edu

This project aims to detect pneumonia from chest X-ray images using a Convolutional Neural Network (CNN). The model is trained on a dataset of chest X-ray images and evaluated for its performance. The project is ongoing, and I aim to fine-tune the model in the future. If you are seeing this, it means I am still working on the project.

Binary classification of breast cancer histopathology images (Benign vs Malignant) using an improved CNN on the BreakHis dataset - 88.24% test accuracy.

This project uses Convolutional Neural Network (CNN) to detect malignant skin cancer moles

This project aims to develop a lung cancer detection system using machine learning techniques. Project Includes Source Code, PPT, Synopsis, Report, Documents, Base Research Paper & Video tutorials

NeuroDetect AI is a deep-learning based system that detects brain tumors and Alzheimer’s disease from MRI scans using optimized CNN and transfer-learning models. It provides fast and reliable predictions through a simple web interface with a Flask backend, offering an effective AI solution for early neurological diagnosis.

This project leverages YOLOv8 and AWS SageMaker to detect lung nodules in CT scan images — an essential step toward early lung cancer diagnosis. The system automates CT image preprocessing, model training, and deployment on SageMaker endpoints using scalable cloud infrastructure.

End-to-end deep learning pipeline for cell segmentation and classification using U-Net and DeepLabV3+ on BBBC038 dataset

Using PIX2PIX for Synthesizing Computerized Tomographic Medical Images

Running Segmentation models for cancer metastasis in brain MR