Abstract: Imbalanced image classification faces critical challenges in balancing the quality and diversity of synthetic minority samples. This article proposes the improved estimation distribution ...

Overview: Master deep learning with these 10 essential books blending math, code, and real-world AI applications for lasting expertise.From neural networks to N ...

To address the class imbalance (in the number of images/masks) between the Hemorrhagic and Ischemic classes of the original CT image dataset, we applied our offline augmentation tools, ...

We will create a Deep Neural Network python from scratch. We are not going to use Tensorflow or any built-in model to write the code, but it's entirely from scratch in python. We will code Deep Neural ...

Running Python scripts is one of the most common tasks in automation. However, managing dependencies across different systems can be challenging. That’s where Docker comes in. Docker lets you package ...

This is the first experiment of Image Segmentation for EBHI-Colorectal-Cancer based on our TensorFlowFlexUNet (TensorFlow Flexible UNet Image Segmentation Model for Multiclass) and, 512x512 pixels ...

Abstract: As one of the core tasks in vision recognition, image classification is widely used in various scenarios. Most existing mainstream image classification models use the Convolutional Neural ...