A patch-based Gastroscopic Classifier web app with Python backend using Flask micro-framework and PyTorch modified Resnet-34 Convolutional Neural Network.
This project was with Viettel Cyberspace Center (VTCC) and CNRS (Centre national de la recherche scientifique).
- Assist Medical Doctors to classify gastric lesion types based on small selected patch on an endoscopy image.
- 6 Lesion types: Active Gastritis, Atrophic Gastritis, Chronic Gastritis, Intestinal Metaplasia, Normal, Ulcer.
- 6-class gastroscopic dataset provided by CNRS (French National Centre for Scientific Research) and 108 Military Central Hospital.
- Lesion classes: Active Gastritis, Atrophic Gastritis, Chronic Gastritis, Intestinal Metaplasia, Normal, Ulcer.
- CNN model: Modified ResNet-34 model with pretrained backbones.
- Multi-perception Layer: 512-256-6.
- Regularization technique: Dropout with p=0.2.
- Backend Language: Python 3.8
- (Core Algorithm) Deep Learning framework: PyTorch
- (Core Algorithm) Image Processing library: OpenCV
- Backend Micro-framework: Flask
- Front-end: pure HTML, CSS, Javascript (no framework).