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 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.

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.

oral_cancer_detection

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.

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

Four Deep Learning Models for COVID-19 X-ray Classification

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

Notebook on the use of the VGG19 model of a Convolutional Neural Network (CNN) for image classification of natural scenes. The framework is TensorFlow

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

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.

This project classifies smoking images using VGG19 with data augmentation, and Captum for model explainability, identifying key features per prediction.

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

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.

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

A web-based platform that detects pneumonia from chest X-rays using AI. It offers accurate analysis, educational resources, and chatbot assistance.

Neural Style Transfer with VGG19 for content and style fusion.