There can be multiple classes for the labeled image. If it belongs to a single class, then we call it recognition; if there are multiple classes, we call it classification. In this article, we use a X ...

CNN consists ... we will use the pre trained VGG16 model and use a dense layer with logistic regression at the end for binary classification. We will analyze the results for the transfer learning with ...

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This paper investigates the challenge of exploiting satellite images using convolutional neural networks (CNN) for settlement classification where the class distribution shifts are significant. We ...

We proposed a CNN-LSTM and improved max value features optimization framework for COVID-19 classification to address these issues. The original images are acquired and the contrast is increased using ...

Abstract: Histopathological image classification stands as a cornerstone in the pathological diagnosis workflow, yet it remains challenging due to the inherent complexity of histopathological images.