Multiclass image classification using Convolutional Neural Network

Making a binary classifier to detect pneumonia using chest x-rays images.

This repo contains the python codes of my final thesis "Analysis of leaf species and detection of diseases using image processing and machine learning methods".

Pre-trained VGG-Net Model for image classification using tensorflow

Full Python Programming Tutorials with Focus on artificial intelligence and machine learning

Pytorch code for recognising 102 different species of flowers using VGG-16 dense network.

A website 🖥 that effectively classifies Covid-19, Pneumonia and Normal Chest X-ray images

Brain tumor detection and prediction using keras vgg-16

Cough detection with Log Mel Spectrogram, Wavelet Transform, Deep learning and Transfer learning techniques

Visualizing the CNN Layer outputs and filters in Keras

Uses retrained MobileNetV2 classification models to determine whether an Indian currency note is fake or real (based on watermark and fluorescent strip)

Deployment of the Omdena Algeria Chapter

Face detection model developed to check discrepancies such as blurred background, spoofed image, obstruction detection, multiple people and other issues in photos uploaded by doctors to the Bajaj Finserv Health App. Winning submission at HackRx2.0.

Image Classification problem, Cats v/s Dogs Model. Browse to https://imgclassification.herokuapp.com/ for the deployment via Heroku

Face recognition using transfer learning

Implementing the MST Research Paper in PyTorch

Fruit image classification using transfer learning and fine tuning

The dataset having Pneumonia and Normal chest X-Ray images were trained on different numbers of epochs to check the variability in the training and validation accuracies. The ResNet50 model with the highest and closest Training and Validation accuracies was then used for the prediction.

Build a flask app that identifies and diagnoses whether chicken has coccidiosis disease or not through fecal sample image.