Medical Image Classification for Disease Diagnosis Using Convolutional Neural Networks Project Summary The project titled "Medical Image Classification for Disease Diagnosis Using Convolutional Neural ...

Image classification finds its suitability in applications ranging from medical diagnostics to autonomous vehicles. The existing architectures are computationally exhaustive, complex and less accurate ...

Image classification finds its suitability in applications ranging from medical diagnostics to autonomous vehicles. The existing architectures are computationally exhaustive, complex and less accurate ...

• A hybrid deep learning approach (i.e., CNN-LSTM with ResNet-152 model) is developed to perform emotion classification using EEG signals linked to PTSD. The activity in the brain appears to have a ...

To better characterize the t -masking impact on CNN classification performance, six different experimental configurations were designed. Moreover, the performances of the presented FS method were ...

Built a deep CNN for multi-class sports image classification using a Kaggle dataset. Tuned hyperparameters (layers, dropout, optimizer, batch size, learning rate, weight decay) and trained with early ...