NeuroScanAI: An AI-Driven Brain Tumor Diagnostic and Support System

Deep learning pipeline for malaria detection using CNNs (VGG16 transfer learning). Achieved 92% accuracy and 93% recall on NIH microscopy dataset. Includes reproducible environment, CLI inference, evaluation plots, CI workflow, and model card.

Dự án xây dựng mô hình CNN và huấn luyện dữ liệu tự cào, website cho phép người dùng nhận diện cầu thủ bóng đá nổi tiếng ở giải ngoại hạng Anh , bằng hình ảnh chụp gương mặt cậu thủ đó

BitBrain: Brain Cancer Detector with Keras in Python

Plant Disease Detection using Deep Learning

Cotton disease detection using CNN

A neural network that transforms a design mock-up into a static website.

This project involves an exploratory analysis of the capabilities of Convolutional Neural Networks (CNNs) for image classification, using a dataset of animal images. The goal is to understand how well CNNs can distinguish between different types of animals based on their images.

RetroSpectra is a real-time facial emotion detection application. It uses Convolutional Neural Network (CNN) to identify human emotions from live video feed. The application leverages a pre-trained model to accurately detect and classify emotions, providing an interactive and engaging user experience.

A Flask app for canvas-written digits recognition

image search engine made using deep learning and vgg model of cnn architecture

The project explored various classification methods. While some methods were developed from scratch to assess the impact of different techniques on improving classification results, others leveraged transfer learning.

A CNN based deep learning model made to detect face masks and helmets (Hard hats as well as bike helmets) which can be used at hospitals and construction sites in order to ensure safety.

Malaria Detection using CNN's

The app is build using CNN and is deplyed on heroku server as a flask-python app.

Image classification demo running on Flask

AIND Term 2 -- CNN Project

Capstone project for Deep Learning Systems

A lot is at stake when a plant is struck with a disease that causes a significant loss in production, economic losses, and a reduction in the quality and quantity of agricultural products. It is crucial to identify plant diseases in order to prevent the loss of agricultural yield and quantity