Image Categorization
11 papers with code • 0 benchmarks • 1 datasets
Benchmarks
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Yeast colony morphologies
Latest papers with no code
A challenge in A(G)I, cybernetics revived in the Ouroboros Model as one algorithm for all thinking
A general lack of encompassing symbol-embedding and (not only) -grounding in some bodily basis is made responsible for current deficiencies.
HyenaPixel: Global Image Context with Convolutions
For image categorization, HyenaPixel and bidirectional Hyena achieve a competitive ImageNet-1k top-1 accuracy of 83. 0% and 83. 5%, respectively, while outperforming other large-kernel networks.
Retinotopic Mapping Enhances the Robustness of Convolutional Neural Networks
This study investigates whether retinotopic mapping, a critical component of foveated vision, can enhance image categorization and localization performance when integrated into deep convolutional neural networks (CNNs).
On the Image-Based Detection of Tomato and Corn leaves Diseases : An in-depth comparative experiments
In Experiment 2, CNN with Batch Normalization achieved disease detection rates of approximately 99. 89% in the training set and val_accuracy values exceeding 97. 52%, accompanied by a val_loss of 0. 103.
Pipeline Enabling Zero-shot Classification for Bangla Handwritten Grapheme
This investigation addresses the complex issue of Optical Character Recognition (OCR) in the specific context of the Bangla language.
Evaluating the Reliability of CNN Models on Classifying Traffic and Road Signs using LIME
The objective of this investigation is to evaluate and contrast the effectiveness of four state-of-the-art pre-trained models, ResNet-34, VGG-19, DenseNet-121, and Inception V3, in classifying traffic and road signs with the utilization of the GTSRB public dataset.
Predicting skull fractures via CNN with classification algorithms
ResNet50, which was used for feature extraction combined with a gradient boosted decision tree machine learning algorithm to act as a classifier for the categorization of skull fractures from brain CT scans into three fracture categories, had the best overall F1-score of 96%, Hamming Score of 95%, Balanced accuracy Score of 94% & ROC AUC curve of 96% for the classification of skull fractures.
Classifications of Skull Fractures using CT Scan Images via CNN with Lazy Learning Approach
We propose a new model called SkullNetV1 comprising a novel CNN by taking advantage of CNN for feature extraction and lazy learning approach which acts as a classifier for classification of skull fractures from brain CT images to classify five fracture types.
DeepMCAT: Large-Scale Deep Clustering for Medical Image Categorization
In recent years, the research landscape of machine learning in medical imaging has changed drastically from supervised to semi-, weakly- or unsupervised methods.
Open-Set Representation Learning through Combinatorial Embedding
Visual recognition tasks are often limited to dealing with a small subset of classes simply because the labels for the remaining classes are unavailable.
