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.

This repository contains an implementation of the ResNet101 architecture in PyTorch.

The Pizza Topping Classification project is centered around developing a deep learning model capable of automatically identifying different pizza toppings from images.

This repository focuses on classifying microscopic images of parasites using deep learning. It features a dataset of 15 parasitic classes, enhanced by Keras's image preprocessing and transfer learning with ResNet. The project aims to improve diagnostic capabilities in medical parasitology and has achieved great results in competitive evaluation.

Landmark Detection using pre-trained models.

Adopted a convolutional neural network for COVID-19 testing. Examined the performance of different pre-trained models on CT testing and identified that larger, out-of-field datasets boost the testing power of the models.

The project leverages transfer learning to classify fashion items using the Zalando dataset. It explores and compares the performance of several convolutional neural network (CNN) architectures, including VGG19, ResNet101V2, DenseNet121, and MobileNetV2.

A deep learning project for classifying seven core emotions from facial expressions using transfer learning on RGB images. Focused on model performance, efficiency, and real-world applicability.