Structural Similarity-Guided Siamese U-Net Model for Detecting Changes in Snow Water Equivalent by Karim Malik, Colin Robertson

“…We conclude with a discussion on the implications of the findings from our study of snow dynamics and climate variables using gridded SWE data, computer vision metrics, and fully convolutional deep neural networks.…”
Get full text

Unmanned Aerial Vehicle-Based Hyperspectral Imaging for Potato Virus Y Detection: Machine Learning Insights by Siddat B. Nesar, Paul W. Nugent, Nina K. Zidack, Bradley M. Whitaker

“…The potato is the third most important crop in the world, and more than 375 million metric tonnes of potatoes are produced globally on an annual basis. …”
Get full text

Role of Artificial Intelligence and Deep Learning in Easier Skin Cancer Detection through Antioxidants Present in Food by Sreevidya R. C., Jalaja G, Sajitha N, D. Lakshmi Padmaja, S. Nagaprasad, Kumud Pant, Yekula Prasanna Kumar

“…Skin cancer is one of the most common types of cancer that has a high mortality rate. …”
Get full text

Enhanced skin cancer diagnosis: a deep feature extraction-based framework for the multi-classification of skin cancer utilizing dermoscopy images by Hadeel Alharbi, Gabriel Avelino Sampedro, Roben A. Juanatas, Se-jung Lim

“…Skin cancer is one of the most common, deadly, and widespread cancers worldwide. …”
Get full text

Advances in ECG and PCG-based cardiovascular disease classification: a review of deep learning and machine learning methods by Asmaa Ameen, Ibrahim Eldesouky Fattoh, Tarek Abd El-Hafeez, Kareem Ahmed

“…It also goes over the most popular datasets used by various diagnostic models (ECG and PCG signals datasets). …”
Get full text

Optimizing Natural Image Quality Evaluators for Quality Measurement in CT Scan Denoising by Rudy Gunawan, Yvonne Tran, Jinchuan Zheng, Hung Nguyen, Rifai Chai

“…The result was obtained using the library of good images. Most are also part of the Convolutional Neural Network (CNN) training dataset against the testing dataset, and a new dataset shows an optimum patch size and contrast levels suitable for the task. …”
Get full text

Using Vision Transformers for Classifying Surgical Tools in Computer Aided Surgeries by El Moaqet Hisham, Janini Rami, Abdulbaki Alshirbaji Tamer, Aldeen Jalal Nour, Möller Knut

“…Nevertheless, it faces challenges due to complex surgical scenes and limited annotated data. Most of the existing methods for classifying surgical tools in laparoscopic surgeries rely on conventional deep learning methods such as convolutional and recurrent neural networks. …”
Get full text

LayerFold: A Python library to reduce the depth of neural networks by Giommaria Pilo, Nour Hezbri, André Pereira e Ferreira, Victor Quétu, Enzo Tartaglione

“…We address typical cases, from fully connected to convolutional layers, discussing constraints and prospective challenges. …”
Get full text

Automated liver segmentation from CT images using modified ResUNet by R.V. Manjunath, Yashaswini Gowda N, H.M. Manu

“…In this study we proposed an automatic system that utilizes convolutional layers to efficiently extract features while maintaining spatial information. …”
Get full text

HDCTfusion: Hybrid Dual-Branch Network Based on CNN and Transformer for Infrared and Visible Image Fusion by Wenqing Wang, Lingzhou Li, Yifei Yang, Han Liu, Runyuan Guo

“…To address these challenges, this paper proposes a dual-branch fusion network combining convolutional neural network (CNN) and Transformer, which enhances the feature extraction capability and motivates the fused image to contain more information. …”
Get full text

CMPF-UNet: a ConvNeXt multi-scale pyramid fusion U-shaped network for multi-category segmentation of remote sensing images by Ning Li, Xiaopeng Yu, Miao Yu

“…Most U-shaped convolutional neural network (CNN) methods have the problems of insufficient feature extraction and fail to fully utilize global/multi-scale context information, which makes it difficult to distinguish similar objects and shadow occluded objects in remote sensing images. …”
Get full text

Deep learning with data transformation improves cancer risk prediction in oral precancerous conditions by John Adeoye, Yuxiong Su

“…Background: Oral cancer is the most common head and neck malignancy and may develop from oral leukoplakia (OL) and oral lichenoid disease (OLD). …”
Get full text

Computer-Aided Grading of Gliomas Combining Automatic Segmentation and Radiomics by Wei Chen, Boqiang Liu, Suting Peng, Jiawei Sun, Xu Qiao

“…Gliomas are the most common primary brain tumors, and the objective grading is of great importance for treatment. …”
Get full text

Identification of temporal anomalies of spectrograms of vibration measurements of a turbine generator rotor using a recurrent neural network autoencoder by V. P. Kulagin, D. A. Akimov, S. A. Pavelyev, E. O. Guryanova

“…An experiment based on the homostatic method of checking the signal with Hamming windows, in the frequency, time and modulation domains and common initial data, allows one to determine the most promising signal characteristics for identification. …”
Get full text

Damage Detection and Identification on Elevator Systems Using Deep Learning Algorithms and Multibody Dynamics Models by Josef Koutsoupakis, Dimitrios Giagopoulos, Panagiotis Seventekidis, Georgios Karyofyllas, Amalia Giannakoula

“…High-quality training data are first generated through multibody dynamics simulations and are then combined with healthy state vibration measurements to train an ensemble of autoencoders and convolutional neural networks for damage detection and classification. …”
Get full text

A dual self-attentive transformer U-Net model for precise pancreatic segmentation and fat fraction estimation by Ashok Shanmugam, Prianka Ramachandran Radhabai, Kavitha KVN, Agbotiname Lucky Imoize

“…Calculating the fat fraction aids in the investigation of β-cell malfunction and insulin resistance. The most widely used pancreas segmentation technique is a U-shaped network based on deep convolutional neural networks (DCNNs). …”
Get full text

Faster Dynamic Graph CNN: Faster Deep Learning on 3D Point Cloud Data by Jinseok Hong, Keeyoung Kim, Hongchul Lee

“…Geometric data are commonly expressed using point clouds, with most 3D data collection devices outputting data in this form. …”
Get full text

Multi-dimensional visual information processing under complex light environments using time-evolved polarization-sensitive synaptic electronics by Yaqian Yang, Wenhao Ran, Ying Li, Yancheng Chen, Di Chen, Guozhen Shen

“…By employing four polarization-state-dependent convolutional kernels, the device demonstrates edge extraction capabilities even under 50% salt pepper noise. …”
Get full text

Highly Robust Synthetic Aperture Radar Target Recognition Method Based on Simulation Data Training by Liping Hu, Canming Yao, Jian Huang, Jinfan Liu, Guanyong Wang

“…Sufficient synthetic aperture radar (SAR) data is the key element in achieving excellent target recognition performance for most deep learning algorithms. It is unrealistic to obtain sufficient SAR data from the actual measurements, so SAR simulation based on electromagnetic scattering modeling has become an effective way to obtain sufficient samples. …”
Get full text

Hollows on Mercury: Creation and Analysis of a Global Reference Catalog With Deep Learning by Valentin T. Bickel, Ariel N. Deutsch, David T. Blewett

“…Abstract Hollows are geologically young depressions on Mercury, most likely associated with the loss of volatile species. …”
Get full text