What are some challenges in deploying CNNs in real-world applications?

Mar 25, 2025 ... We introduce CNN-TumorNet, an innovative method for brain tumor classification utilizing a deep convolutional neural network (CNN) for the ... Nov 28, 2022 ... In this study, different deep CNN models with multiple machine learning classifiers are investigated for monkeypox disease diagnosis using ... Apr 12, 2025 ... CNN in deep learning excels at image classification, which involves sorting images into predefined categories. They can effectively identify ... We call this approach GRAM-CNN. To automatically label a word, this method uses the local information around a word. Therefore, the GRAM-CNN method does not ... May 25, 2021 ... A Multipath Convolutional Neural Network (M-CNN) is used for global and local feature extraction from images. Then, a machine learning ... A deep neural network (DNN) is an artificial neural network with multiple hidden layers of units between the input and output layers. With the development of deep learning, object detection has achieved very promising results. There are several algorithms and models, such as R-CNN, Faster R- ... Sep 30, 2024 ... I'm a CS student and have already studied and implemented "normal" Neural Networks, as well as many other machine learning algorithms, so I have ... Jul 21, 2022 ... Predicting wind power generation using machine learning and CNN-LSTM approaches ... Hyperparameters are adjusted using all the machine learning ... A Convolutional Neural Network (CNN) is a type of deep learning model designed primarily for processing grid-like data,