This project evaluates and compares the prediction performances of various state of art pre-trained image classification models in classifying 5 types of flowers.

this project is based on brain tumor detection using image classification and deep learning models like CNN , KNN , Logistic Regression , XG-Boost , Random Forest and RESNET50V2. After testing these 6 models the best model among these 6 with high accuracy is taken and trained to the model and predicted the out put

Classify image as {early blight, late blight ,healthy}

This repository hosts the Cervical Cancer Image Classification project, a comprehensive effort aimed at improving the classification accuracy of Squamous Cell Carcinoma (SCC) through advanced deep learning models and ensemble techniques. The project utilizes the Herlev dataset.

Retina OCT Images Different Model Train

Semester project for the Deep Learning class of the MSc in Artificial Intelligence

A project is a second part of Introduction to Machine Learning course. Here the problem is image recognition on CIFAR10 dataset using 2 different approaches: simple model and transfer learning using pretrained model.

This Dog Breed Classification model employs TensorFlow 2.0 and Transfer Learning with ResNet50v2 to accurately identify 70 different dog breeds from images, demonstrating the power of deep learning in image classification tasks.

Deep convolutional neural networks for brain tumor detection leveraging transfer learning and optimization through fully convolutional neural networks.

Klasifikasi Kanker otak dengan 7 model CNN

Multiclass Classification of Imbalanced Image Dataset using Transfer Learning.

This research enhances early disease diagnosis by analyzing retinal blood vessels in fundus images using deep learning. It employs eight pre-trained CNN models and Explainable AI techniques.