Both models (segmentation and classification) are trained independently and non-recursively. However, segmented images from the first step (segmentation model) are used to train the classification ...

The problem of insufficient samples has been limiting the performance of intelligent interpretation in Synthetic Aperture Radar (SAR) images. Humans have the ability to recognize new instances with ...

This repository contains code for a binary image classification model to detect pneumothorax using the ResNet-50 V2 architecture. It includes essential steps such as dataset splitting, image ...

Recently, many computer-aided diagnosis (CAD) methods have been proposed to help physicians automatically classify endoscopic images. However, most existing methods often result in poor performance, ...

The binary classification technique presented in this article uses a single output node with sigmoid () activation and BCELoss () during training. It is possible to view a binary classification ...

Binary Classification Using New PyTorch Best Practices, Part 2: Training, Accuracy, Predictions Dr. James McCaffrey of Microsoft Research explains how to train a network, compute its accuracy, use it ...