This repository contains code and resources for detecting pneumonia from chest X-ray images using the InceptionV3 deep learning model. The project uses PyTorch for model development and training.

Official Repository for the paper "Feature Extraction for Generative Medical Imaging Evaluation: New Evidence Against an Evolving Trend".

This study focuses on four deep-learning models, which are Inception V3, MobileNet V2, ResNet152V2, and VGG19, aiming to enhance the accuracy of tumor Classification

Implementation of popular deep learning networks with TensorRT network definition API

Accurate monitoring of fetal heart rate (FHR) is essential for assessing fetal well-being during pregnancy and labour. However, there is an important challenge: FHR sound recordings include noise. Current solutions tend to discount memory simplicity and real-time processing in favour of traditional signal processing techniques. Our study addresses

Satellite Image Classification

Automatically detecting the presence of dental open bite using traditional convolutional neural network (CNN) and transfer learning

Blindness Detection - Machine Learning Model for Diabetic Retinopathy Patients

🎉A comprehensive project for skin cancer detection using a CNN model.

Various codes and scripts used during AI research. Orginally developed in the Binary_label_predictions_with_CNNs repository

Various codes and scripts used during AI research, all neatly organised

This repo is for Image Classification of butterfly images of 10 classes using Transfer Learning. Different Pre-trained DL models were used for Transfer Learning. Also, flask was used to create a front end.

Cataract detection model

The CGI2Real_Multi-Class_Image_Classifier categorizes humans, horses, or both using transfer learning from Inception CNN. Trained on synthetic images, it can also classify real ones.

Collections of Projects in the Domain of Data Science and Machine Learning

DeepFake Image Classifier Using Deep learning

A python library built to empower developers to build applications and systems with self-contained Computer Vision capabilities

Image forgery detection using CNN fusion model achieving 85% test accuracy. Features ELA preprocessing and fusion of InceptionV3, VGG16, and MobileNetV2. Ideal for digital forensics.