Skin Cancer Detection Web App using Flask Framework deployed on the Heroku server.

Models Supported: DenseNet121, DenseNet161, DenseNet169, DenseNet201 and DenseNet264 (1D and 2D version with DEMO for Classification and Regression)

Ensemble based transfer learning approach for accurately classifying common thoracic diseases from Chest X-Rays

Static malware detection using transfer learning techniques on MMCC_2015 dataset.

Analysis of Abnormality in Humerus X-Ray images using DenseNet

Towards Online Waypoint Generation for a Quadrotor Using Enhanced Monocular Depth Estimation.

🤳🧱 What the F.. Brick ?! A powerful tool that detects up the Lego bricks out of your picture !

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.

Replicated results from DenseDepth using DenseNet169 in Python.

Notebooks made for the Kaggle Global Wheat Detection Competition

This repo was developed for the Alzheimer disease diagnosis.

Trabajo Fin de Máster: Estudio comparativo de un clasificador de imágenes en Raspberry Pi, de forma que se compara el tiempo de la inferencia en la Raspberry Pi con y sin el Neural Compute Stick (NCS). También se estudia como la complejidad de una red neuronal repercute en el tiempo de inferencia y se analiza si los tiempos obtenidos con el NCS …

Exploring the Application of Attention Mechanisms in Conjunction with Baseline Models on the COVID-19-CT Dataset

https://www.youtube.com/watch?v=Qmjg0hL7axs

Image classification using deep learning models

To predict if a person has pneumonia or not using Chest X-Ray.

Brain tumor classification based on MGMT methylation status present on the tumor cell.

This project implements and compares two deep learning architectures (ResNet50 and DenseNet169) for classifying glomeruli images into globally sclerotic and non-globally sclerotic categories.