Monografía presentada para optar al título de Especialista en Analítica y Ciencia de Datos

Streamlit web-app based Bone Fracture detection using YoloV8, FasterRCNN with ResNet, and VGG16 with SSD

This project uses an ensemble of CNN, RNN, and VGG16 models to enhance CIFAR-10 image classification accuracy and robustness. By combining multiple architectures, we significantly outperform single-model approaches, achieving superior classification performance.

🌻Flower Recognition using Multi-class classification 🌻

Diabetic Retinopathy Detection

Contains Deep Learning Content and Algorithm. ANN_CNN_RNN(LSTM-GRU)_AUTOENCODER

Pneumo.ai is a Streamlit web app for detecting pneumonia from X-rays, leveraging CNN variants like AlexNet, VGG, and ResNet18, with ResNet18 leading with an accuracy of 0.89. It employs early stopping callbacks for efficient model training, visualizes training using TensorBoard, and optimizes inference time with pruning and quantization techniques.

end to end classification project , to check whether chicken is healthy or affeccted with coccidiosis based on chicken fecal image collected , Model used - VGG16

A machine learning model for the early detection of glaucoma from color fundus photographs (CFPs), aiming to mitigate one of the leading causes of irreversible blindness

An AI model that Classifies between 4 classes of Brain Tumors. Well-established CNN architecture pre-trained on a massive dataset of MRI scans. VGG16 model is used for this task.

The Brain Tumor MRI Dataset from Kaggle is employed for automated brain tumor detection and classification research. Investigated methods include using pre-trained models (VGG16, ResNet50, and ViT). 🧠🔍

Detecting and diagnosing various skin lesions using CNN

This repo is for Image Classification of butterfly images of 10 classes using Transfer Learning. Different Pre-trained DL models were used for Transfer Learning. Also, flask was used to create a front end.

Comparative Analysis of Deep Learning Models for Pneumonia Detection in Chest X-ray Images: A Game Changer in Improving Diagnosis and Patient Outcomes

A pre-trained image classifier to classify dog breeds. Udacity Nanodegree project-1.

DiNeSys is a distributed system built for deep learning network profiling on cloud-edge systems, developed with Tensorflow (CNN computational part) Apache Thrift (client/server structure)

Coryza Disease Prediction for Chickens is a mobile application that predicts the likelihood of Infectious Coryza in poultry. Utilizing the VGG16 architecture in machine learning, it provides advanced data analysis for early detection and intervention, ensuring the health and productivity of chicken flocks.

This repository hosts code for a deep learning project focused on classifying chest X-ray images into normal and abnormal categories, with a specific emphasis on detecting COVID-19 and pneumonia cases. Leveraging convolutional neural networks (CNNs) and transfer learning methodologies, the project aims to achieve precise classification outcomes.

Develop a classifier to differentiate between real and AI-generated images of damaged cars, identifying actual accidents from fraudulent ones.

Brain Tumor Detection Project with HaarCascade, Convolution Neural Network and OpenCV