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".

BASED ON BRAIN MRI IMAGES DATASET WE NEED CLASSIFY THE BRAIN TUMOUR

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.

The Eye Disease Classification project aims to develop a robust model for the automated classification of retinal images . Leveraging a diverse dataset sourced from reputable repositories, the project employs a Convolutional Neural Network (CNN) architecture, with a focus on utilizing the pre-trained VGG19 model.

Classifying MonkeyPox Images using various Deep Learning networks (VGG16,VGG19,RESNET50 and a Custom CNN model)

Feature Extraction on the Rail Lines Using Semantic Segmentation and Self-supervised Learning.

Breast Cancer H&E classification of Images and Image Generation

This project aims to detect and classify forest fires using deep learning techniques, specifically the VGG-19 convolutional neural network model. The model is trained to analyze images and accurately predict whether they contain signs of a forest fire or not.

Trap Camera with positioning system and classification of images by animal species

A PyTorch project implementing neural style transfer using the VGG19 model, combining the content of one image with the style of another for artistic transformations.

The GitHub repo on "Image Stylization using VGG19" has an implementation of neural style transfer using VGG19, allowing users to apply the style of one image to the content of another. It includes pre-trained models for easy use, enabling users to experiment with different styles and content images for creative and visually appealing results.

we used publicly available datasets to apply some deep learning modeling techniques

Neural style transfer to merge content and style images using pre-trained VGG19 model

To improve the accuracy and speed of malaria diagnosis, the project aims to distinguish Malaria infected human blood cells from the normal ones.

Malaria prediction using VGG19

The goal of this project is to study and implement VGG19 based classification model for Flowers' dataset.

We use a pre-trained model, VGGNet in our case for the python implementation of style transfer.

Embark on a machine learning journey with this Python project focusing on automated image classification. The implementation utilizes the VGG19 architecture in Keras, enhancing the model's ability to classify images across 38 different classes. Augmentation techniques like zooming, shearing, and horizontal flipping are employed for a robust dataset

PLANT LEAF DISEASE PREDICTION