This project demonstrates the implementation of a Support Vector Machine (SVM) for image classification using the CIFAR-10 dataset. The code trains an SVM model with a linear kernel and evaluates its ...

Application of a hybrid EfficientNet-SVM model to medical image classification Abstract: Within the realm of medical science, imaging stands out as the most potent diagnostic and therapeutic tool.

SVM can be a useful tool for medical image processing and diagnosis, however it has some limitations. It can be sensitive to the choice of hyperparameters and kernel functions, and be affected by ...

The original AlexNet was designed for ImageNet classification, which takes in 224 x 224 x 3 images. To fit our 64 x 64 x 3 images from Tiny ImageNet, we can either modify the architecture of the ...

In this article, proposed a CNN based architecture to classify the forgery in given image. CNN architecture is capable to detect the unseen forgeries based on features extracted through various ...

To apply your SVM model, you need to use appropriate tools and frameworks that support SVM for image classification. One tool is scikit-learn, which is a popular Python library for machine learning.