Laith Alzubaidi

TL;DR: In this paper, a comprehensive survey of the most important aspects of DL and including those enhancements recently added to the field is provided, and the challenges and suggested solutions to help researchers understand the existing research gaps.

TL;DR: A deep convolutional neural network (DCNN) model that integrates three ideas including traditional and parallel Convolutional layers and residual connections along with global average pooling is designed that can significantly improve the performance considering a reduced number of images in the same domain of the target dataset.

TL;DR: A novel transfer learning approach to overcome the previous drawbacks by means of training the deep learning model on large unlabeled medical image datasets and by next transferring the knowledge to train the deepLearning model on the small amount of labeled medical images is proposed.

TL;DR: This research proposes lightweight deep learning models that classify the erythrocytes into three classes: circular (normal), elongated (sickle cells), and other blood content, which are different in the number of layers and learnable filters.

TL;DR: This review investigates and analyses the most recent methods, developed over three years leading up to 2020, for training, augmentation, feature fusion and extraction, recognising and counting crops, and detecting plant diseases, including how these methods can be harnessed to feed deep classifiers and their effects on classifier accuracy.