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This project is an image classification project using a deep-learning based on Convolutional Neural Networks (CNNs) with Keras. The Dogs vs. Cats is a classic problem for anyone who wants to dive deeper into deep-learning.
Explores the use of a simple CNN model for the detection of COVID-19 in X-ray images. Leveraging a dataset from Kaggle, the pipeline includes preprocessing, model architecture design, performance evaluation, and testing on new cases.
The project includes a detailed explanation of the CNN and ICP algorithms, along with their implementation in Python using popular deep learning and computer vision libraries such as TensorFlow, Keras, and OpenCV. The dataset used for training and testing the models is the CIFAR-10 dataset, which consists of 60,000 32x32 color images in 10 classes.
Build end-to-end DL pipeline for computer vision (Image classification) for “Chest Disease Classification from Chest CT Scan Images” and deploy Flask web app to AWS EC2 with Docker and CI/CD tool: Jenkins
This project consists of following steps: - dataset creation with web scraping (BeautifulSoup) - data pre-processing (DifPy, Rembg, Keras) - CNN deep learning model training (used MobileNet V2 model) and training (Tensorflow) - testing the model and analyzing the results.