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.

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

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

Proyek ini adalah aplikasi web full-stack yang dirancang untuk deteksi otomatis penyakit pada daun padi. Pengguna dapat mengunggah gambar daun padi, dan sistem akan menganalisisnya menggunakan model machine learning yang telah dilatih sebelumnya.

Breast cancer classification using ensemble deep learning with VGG16 and ResNet50V2 on CBIS-DDSM mammogram dataset

Multiclass Classification of Imbalanced Image Dataset using Transfer Learning.

Dự án nghiên cứu khoa học sinh viên HVNH 2024 - 2025

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.

Master's Thesis project: Coffee bean roasting classification using ResNet50V2.

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.

Deep Learning system for Diabetic Retinopathy detection using EfficientNetB3, DenseNet201 & ResNet50V2 with Explainable AI (Grad-CAM & Score-CAM). Achieves 96% accuracy

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

Klasifikasi Kanker otak dengan 7 model CNN

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 repository contains a fine-tuned Convolutional Neural Network Resnet50v2 model training for corneal eye ulcer classification. This training also shows the effect of data leakage on the separation of training data and validation data which can give overly optimistic results.

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.

Ship classification for the minor project

🥭 MangiferaNet: A Deep Learning-based web application for diagnosing mango leaf diseases using MobileNetV2 and Streamlit.

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

AI-powered web app for skin disease classification using ResNet50V2 deep learning. Identifies Acne, Eczema, Psoriasis, Vitiligo & Warts from images. Features Flask interface, real-time predictions, and LIME explainability.