The explosive growth of data generated by artificial intelligence, cloud computing and modern digital infrastructure is ...
Abstract: Diagnosing brain tumors is challenging for radiologists because of the significant similarities between the tumor types. Deep learning models lack sufficient data to effectively learn the ...
Classifying video presents unique challenges for machine learning models. As I’ve covered in my previous posts, video has the added (and interesting) property of temporal features in addition to the ...
1 Amazon Web Services, Seattle, USA. 2 Rajiv Gandhi University of Knowledge Technologies, Nuzvid, India. Optical Coherence Tomography (OCT) is a non-invasive imaging modality widely employed for ...
The ESC-50 dataset is a labeled collection of 2000 environmental audio recordings suitable for benchmarking methods of environmental sound classification. The dataset consists of 5-second-long ...
Deep learning-based classification of eye diseases using Convolutional Neural Network for OCT images
Deep learning shows promising results in extracting useful information from medical images. The proposed work applies a Convolutional Neural Network (CNN) on retinal images to extract features that ...
Identification of leaf diseases plays an important role in the growing process of different types of plants. Current studies focusing on the detection and categorization of leaf diseases have achieved ...
This is the official code for the "Which Augmentation Should I Use? An Empirical Investigation of Augmentations for Self-Supervised Phonocardiogram Representation Learning", accepted in IEEE Access.
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