22 papers · ranked by Valyu relevance
Mingxing Tan, Quoc V. Le
Convolutional Neural Networks (ConvNets) are commonly developed at a fixed resource budget, and then scaled up for better accuracy if more resources are available. In this paper, we systematically study model scaling and identify that carefully balancing network depth, width, and resolution can lead to better…
Madallah Alruwaili, Mahmood Mohamed, Wan Azani Mustafa, Hiam Alquran
Background: Medical diagnosis for skin diseases, including leukemia, early skin cancer, benign neoplasms, and alternative disorders, becomes difficult because of external variations among groups of patients. A research goal is to create a fusion-level deep learning model that improves stability and skin disease…
Xiaodi Pu, Longyi Liu, Yonglai Zhou, Zihan Xu
In the field of biomedical research, rats are widely used as experimental animals due to their short gestation period and strong reproductive ability. Accurate monitoring of the estrous cycle is crucial for the success of experiments. Traditional methods are time-consuming and rely on the subjective judgment of…
Guilherme Vieira Neto, Marcos Eduardo Valle
—EfficientNet models are convolutional neural networks optimized for parameter allocation by jointly balancing network width, depth, and resolution. Renowned for their exceptional accuracy, these models have become a standard for image classification tasks across diverse computer vision benchmarks. While traditional…
Yanan Zhao, Chunshen Long, Na Yin, Zhihao Si + 3 more
Spatial Transcriptomics (ST) leverages Gene Expression Profiling while preserving Spatial Location and Histological Images, enabling it to provide new insights into tissue structure, tumor microenvironment, and biological development. The identification of spatial domains serves as not only the foundation for ST…
Qaisar Abbas, Yassine Daadaa, Umer Rashid, Muhammad Zaheer Sajid + 2 more
Hypertensive retinopathy (HR) and diabetic retinopathy (DR) are retinal diseases closely associated with high blood pressure. The severity and duration of hypertension directly impact the prevalence of HR. The early identification and assessment of HR are crucial to preventing blindness. Currently, limited…
Romario Sameh Samir
Accurate and efficient classification of different types of cancer is critical for early detection and effective treatment. In this paper, we present the results of our experiments using the EfficientNet algorithm for classification of brain tumor, breast cancer mammography, chest cancer, and skin cancer. We used…
Renfeng Liu, Haonan Dai, YingYing Chen, Hongxing Zhu + 4 more
Hail, a highly destructive weather phenomenon, necessitates critical identification and forecasting for the protection of human lives and properties. The identification and forecasting of hail are vital for ensuring human safety and safeguarding assets. This research proposes a deep learning algorithm named Dual…
Arissa Wongpanich, Hieu Pham, James Demmel, Mingxing Tan + 3 more
'Quoc V. Le' 'Yang You' 'Sameer Kumar'] EfficientNets are a family of state-of-the-art image classification models based on efficiently scaled convolutional neural networks. Currently, EfficientNets can take on the order of days to train; for example, training an EfficientNet-B0 model takes 23 hours on a Cloud TPU v2-8…
Shuang Hu, Jin Liu, Zhiwei Kang, Felipe Jiménez
Due to the complexity and danger of Mars’s environment, traditional Mars unmanned ground vehicles cannot efficiently perform Mars exploration missions. To solve this problem, the DeepLabV3+/Efficientnet hybrid network is proposed and applied to the scene area judgment for the Mars unmanned vehicle system. Firstly…
Hironori Takimoto, Yasuhiro Sato, Atsushi J. Nagano, Kentaro K. Shimizu + 1 more
Recently, deep convolutional neural networks (CNN) have been adopted to help beginners identify insect species from field images. However, the application of these methods on the identification of tiny congeneric species moving across heterogeneous background remains difficult. To enable rapid and automatic…
Sedrick Scott Keh
In recent years, deep learning has vastly improved the identification and diagnosis of various diseases in plants. In this report, we investigate the problem of pathology classification using images of a single leaf. We explore the use of standard benchmark models such as VGG16, ResNet101, and DenseNet 161 to achieve a…
Xu Zhang, Fuji Lai, Weisi Chen, Chengyuan Yu
Deep learning has received considerable attention in the computer vision field and has been widely studied, especially in recognizing and diagnosing ophthalmic diseases. Currently, glaucoma recognition algorithms are mostly based on unimodal OCT, the visual field for glaucoma auxiliary diagnosis. Such algorithms have…
Gopal Sangar, Velswamy Rajasekar
Introduction Potatoes are a vital global product, and prompt identification of foliar diseases is imperative for sustaining healthy yields. Computer vision is essential in precision agriculture, facilitating automated disease diagnosis and decision-making through real-time data. Inconsistent data in uncontrolled…
H. Abbasi, S.R Mollet, S.A. Williams, L. Lim + 3 more
The presence of abnormal infant General Movements (GMs) is a strong predictor of progressive neurodevelopmental disorders, including cerebral palsy (CP). Automation of the assessment will overcome scalability barriers that limit its delivery to at-risk individuals. Here, we report a robust markerless pose-estimation…
Jiayi Li, Haiyan Zeng, Chenxin Huang, Libin Wu + 4 more
'Beibei Zhou' 'Dapeng Ye' 'Haiyong Weng'] Salt stress is considered one of the primary threats to cotton production. Although cotton is found to have reasonable salt tolerance, it is sensitive to salt stress during the seedling stage. This research aimed to propose an effective method for rapidly detecting salt stress…
Veronika Koren, Simone Blanco Malerba, Tilo Schwalger, Stefano Panzeri
The principle of efficient coding posits that sensory cortical networks are designed to encode maximal sensory information with minimal metabolic cost. Despite the major influence of efficient coding in neuroscience, it has remained unclear whether fundamental empirical properties of neural network activity can be…
Md. Mohaimenul Hossain, Jean-Philippe Georges, Eric Rondeau, Thierry Divoux
'Thierry Divoux'] There are all sort of indications that Internet usage will go only upwards, resulting in an increase in energy consumption and CO2 emissions. At the same time, a significant amount of this carbon footprint corresponds to the information and communication technologies (ICT) sector, with around one…
Authors not listed
This research presents a novel approach to obstacle detection during navigation using a combination of Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. The primary objective is to generate accurate image captions that describe the content of images, which is crucial for applications such…
Junaid Qadir, Arjuna Sathiaseelan, Liang Wang, Barath Raghavan
Decades of experience have shown that there is no single one-sizefits-all solution that can be used to provision Internet globally and that invariably there are tradeoffs in the design of Internet. Despite the best efforts of networking researchers and practitioners, an ideal Internet experience is inaccessible to an…
Kaeser M. Sabrin, Yongbin Wei, Martijn van den Heuvel, Constantine Dovrolis
We approach the C. elegans connectome as an information processing network that receives input from about 90 sensory neurons, processes that information through a highly recurrent network of about 80 interneurons, and it produces a coordinated output from about 120 motor neurons that control the nematode’s muscles. We…
Sergio Pablo-García, Raúl Pérez-Soto, Albert Sabadell-Rendón, Diego Garay-Ruiz + 2 more
In the study of chemical reactions, visualizing reaction networks is pivotal for identifying crucial compounds and reactions. Traditional methods, such as network schematics and reaction path linear plots, often struggle to effectively represent complex reaction networks due to their size and intricate connectivity.…