resnet50v2

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.

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

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.

Breast ultrasound image segmentation app using a U-Net with ResNet50V2 backbone, featuring an interactive Gradio demo and AI-based lesion detection.

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

Multiclass Classification of Imbalanced Image Dataset using Transfer Learning.

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.

Collection of AI, Machine Learning, and Deep Learning projects covering plant disease detection, causal inference, customer churn prediction, heart disease diagnosis, and medical cost forecasting using TensorFlow, Scikit-learn, Random Forest, ResNet50, and statistical learning techniques.

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

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

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

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

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

Klasifikasi Kanker otak dengan 7 model CNN

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.

Ship classification for the minor project

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.

A deep learning pipeline for detecting COVID-19 from chest X-rays using ResNet50V2 with Grad-CAM. The project covers data exploration, preprocessing, augmentation, model training, evaluation, and interpretability, demonstrating accurate, explainable classification across multiple radiography classes.