Evaluating CNN Architectures and Hyperparameter Tuning for Enhanced Lung Cancer Detection Using Transfer Learning by Mohd Munazzer Ansari, Shailendra Kumar, Umair Tariq, Md Belal Bin Heyat, Faijan Akhtar, Mohd Ammar Bin Hayat, Eram Sayeed, Saba Parveen, Dustin Pomary

“…This study evaluates the performance of six convolutional neural network (CNN) architectures, ResNet-50, VGG-16, ResNet-101, VGG-19, DenseNet-201, and EfficientNet-B4, using the LIDC-IDRI dataset. …”
Get full text

Studying Forgetting in Faster R-CNN for Online Object Detection: Analysis Scenarios, Localization in the Architecture, and Mitigation by Baptiste Wagner, Denis Pellerin, Sylvain Huet

“…In this context, the widely used architecture Faster R-CNN (Region Convolutional Neural Network) faces catastrophic forgetting: the acquisition of new knowledge leads to the loss of previously learned information. …”
Get full text

Automated karyogram analysis for early detection of genetic and neurodegenerative disorders: a hybrid machine learning approach by Sumaira Tabassum, M. Jawad Khan, Javaid Iqbal, Asim Waris, M. Adeel Ijaz

“…It is fine-tuned on labeled data, followed by a classification step using a Convolutional Neural Network (CNN). A unique dataset of 234,259 chromosome images, including the training, validation, and test sets, was used. …”
Get full text

Nf-Root: A Best-Practice Pipeline for Deep-Learning-Based Analysis of Apoplastic pH in Microscopy Images of Developmental Zones in Plant Root Tissue by Julian Wanner, Luis Kuhn Cuellar, Luiselotte Rausch, Kenneth W. Berendzen, Friederike Wanke, Gisela Gabernet, Klaus Harter, Sven Nahnsen

“…In short, a deep-learning module deploys deterministically trained convolutional neural network models and augments the segmentation predictions with measures of prediction uncertainty and model interpretability, while aiming to facilitate result interpretation and verification by experienced plant biologists. …”
Get full text

Transforming oral cancer care: The promise of deep learning in diagnosis by Durairaj Varalakshmi, Mayakrishnan Tharaheswari, Thirunavukarasou Anand, Konda Mani Saravanan

“…Specifically, we explore the efficacy of convolutional neural networks (CNNs) and recurrent neural networks (RNNs) in diagnosing and predicting the prognosis of oral cancer in the last five years. …”
Get full text

HSSCIoT: An Optimal Framework Based on Internet of Things-Cloud Computing for Healthcare Services Selection in Smart Hospitals by Oanh Nguyen

“…The combination of Recurrent Neural Networks (RNN) means Long Term Short Memory (LSTM) and new kinds of Convolutional Neural Networks (CNN) means Atrous Spatial Pyramid Pooling (ASPP) deep learning methods considered for HSSCIoT.…”
Get full text

Anatomy-Informed Multimodal Learning for Myocardial Infarction Prediction by Ivan-Daniel Sievering, Ortal Senouf, Thabo Mahendiran, David Nanchen, Stephane Fournier, Olivier Muller, Pascal Frossard, Emmanuel Abbe, Dorina Thanou

“…<italic>Methods:</italic> The images are analyzed by Convolutional Neural Networks (CNNs) guided by anatomical information, and the clinical data by an Artificial Neural Network (ANN). …”
Get full text

A Step-by-Step Guide for Automated Plant Canopy Delineation Using Deep Learning: An Example in Strawberry Using ArcGIS Pro Software by Amr Abd-Elrahman, Katie Britt, Vance Whitaker

“…Anyone with basic geographic information system analysis skills may follow along with the demonstration and learn to implement the Mask Region Convolutional Neural Networks model, a widely used model for object detection, to delineate strawberry canopies using ArcGIS Pro Image Analyst Extension in a simple workflow. …”
Get full text

Automatic etiological classification of stroke thrombus digital photographs using a deep learning model by Álvaro Lucero-Garófano, Álvaro Lucero-Garófano, Alicia Aliena-Valero, Isabel Vielba-Gómez, Isabel Vielba-Gómez, Irene Escudero-Martínez, Irene Escudero-Martínez, Lluís Morales-Caba, Lluís Morales-Caba, Fernando Aparici-Robles, Fernando Aparici-Robles, Diana L. Tarruella Hernández, Diana L. Tarruella Hernández, Gerardo Fortea, Gerardo Fortea, José I. Tembl, José I. Tembl, Juan B. Salom, Juan B. Salom, José V. Manjón

“…Cryptogenic thrombi were classified as cardioembolic (96%) or atherothrombotic (4%).ConclusionTwo convolutional neural networks perform the automatic segmentation of thrombus images and, combined with selected clinical characteristics, their accurate and precise classification into atherothrombotic or cardioembolic etiology in patients with acute ischemic stroke.…”
Get full text

A survey of image object detection algorithm based on deep learning by Tingting ZHANG, Jianwu ZHANG, Chunsheng GUO, Huahua CHEN, Di ZHOU, Yansong WANG, Aihua XU

“…Image object detection is to find out the objects of interest in the image and determine their classifications and locations.It is a research hotspot in the field of computer vision.In recent years,due to the significant improvement in the accuracy of image classification with deep learning,image object detection models based on deep learning have gradually became mainstream.Firstly,the convolutional neural networks commonly used in image object detection were briefly introduced.Then,the existing classical image object detection models were reviewed from the perspective of candidate regions,regression and anchor-free methods.Finally,according to the detection results on the public dataset,the advantages and disadvantages of the models were analyzed,the problems in the image object detection research were summarized and the future development was forecasted.…”
Get full text

Research on berth occupancy prediction model based on attention mechanism by Zhurong WANG, Wei XUE, Yabang NIU, Ying’an CUI, Qindong SUN, Xinhong HEI

“…To solve the problem that the berth occupancy prediction accuracy decreases while the prediction step was increasing, a berth occupancy prediction model based on an attention mechanism was proposed, which was the multivariate time pattern information obtained by convolutional neural networks (CNN).The characteristic information was learned by the model training, and the sequence with higher correlation was assigned a larger learning weight, so that the highly correlated features output from the decoder could be used to predict the target sequence.Data sets of multiple parking lot were adopted to test the model.The test results show that the proposed model can estimate the real value well when the step length of berth occupancy prediction reaches 36.The prediction accuracy and stability of the model are improved compared with long short-term memory (LSTM) model.…”
Get full text

A novel lightweight model for tea disease classification based on feature reuse and channel focus attention mechanism by Junjie Liang, Renjie Liang, Dongxia Wang

“…To improve the recognition accuracy, the traditional classic convolutional neural network (CNN) models require higher model complexity. …”
Get full text

Nerve‐Inspired Optical Waveguide Stretchable Sensor Fusing Wireless Transmission and AI Enabling Smart Tele‐Healthcare by Tianliang Li, Qian'ao Wang, Zichun Cao, Jianglin Zhu, Nian Wang, Run Li, Wei Meng, Quan Liu, Shifan Yu, Xinqin Liao, Aiguo Song, Yuegang Tan, Zude Zhou

“…A speech recognition and human‐machine interaction system, based on sensor signal acquisition, is constructed, and the convolutional neural network algorithm is integrated for disease assessment. …”
Get full text

A multi-modal geospatial–temporal LSTM based deep learning framework for predictive modeling of urban mobility patterns by Sangeetha S.K.B, Sandeep Kumar Mathivanan, Hariharan Rajadurai, Jaehyuk Cho, Sathishkumar Veerappampalayam Easwaramoorthy

“…The model also shows substantial improvements over traditional techniques, including Convolutional LSTM and Graph Convolutional Networks. …”
Get full text

Cross-Spectral Cross-Distance Face Recognition via CNN with Image Augmentation Techniques by Nisa Adilla Rahmatika, Fitri Arnia, Maulisa Oktiana

“…This study aims to evaluate the performance of Convolutional Neural Networks (CNNs) in addressing the Cross-Spectral Cross-Distance (CSCD) challenge, which involves face identification across different spectra (NIR and VIS) and varying distances. …”
Get full text

A Large-Scale Spatio-Temporal Multimodal Fusion Framework for Traffic Prediction by Bodong Zhou, Jiahui Liu, Songyi Cui, Yaping Zhao

“…Specifically, we utilize Convolutional Neural Networks (CNNs) for spatial information processing and a combination of Recurrent Neural Networks (RNNs) for final spatio-temporal traffic prediction. …”
Get full text

Lung Diseases Diagnosis-Based Deep Learning Methods: A Review by Shahad A. Salih, Sadik Kamel Gharghan, Jinan F. Mahdi, Inas Jawad Kadhim

“…This review discusses the various DL methods that have been developed for lung disease diagnosis, including convolutional neural networks (CNNs), deep neural networks (DNNs), and generative adversarial networks (GANs). …”
Get full text

Improving Health Through Indoor Environmental Quality Monitoring: A Review of Data-Driven Models and Smart Sensor Innovations by Kidari Rachid, Tilioua Amine

“…Numerous cutting-edge deep learning techniques, including convolutional neural networks (CNNs), long short-term memory networks (LSTMs), decision trees (DTs), support vector machines (SVMs), artificial neural networks (ANNs), and deep neural networks (DNNs), are incorporated into the hybrid framework. …”
Get full text

On the assessment and reliability of political and ideological education in colleges using deep learning methods by Yongsheng Ma, Xianhui Sun, Aiqun Ma

“…Sophisticated deep learning techniques including artificial neural networks (ANN), convolutional neural networks (CNN), and support vector machines (SVM) were utilized to enhance the reliability of these evaluations. …”
Get full text

Fault Diagnosis of Planetary Gearbox Based on Motor Current Signal Analysis by Ziyuan Jiang, Qinkai Han, Xueping Xu

“…Subsequently, four classical machine learning models, including the support vector machine (SVM), decision tree (DT), random forest (RF), and AdaBoost, are used for fault classifications based on the features extracted via principal component analysis (PCA). The convolutional neural network (CNN), which can automatically extract features, is also adopted. …”
Get full text