Relevant bibliographies by topics / ResNet18

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'ResNet18'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "ResNet18"

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Zhang, Tanjing. "Performance Analysis of Residual Networks for Pneumonia Diagnosis on Chest X-Rays." Applied and Computational Engineering 138, no. 1 (2025): 104–9. https://doi.org/10.54254/2755-2721/2025.21363.

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ResNets have demonstrated exceptional capabilities in image classification tasks, making them a promising tool for clinical applications. This study evaluates the performance of deep learning Residual Networks (ResNets) in classifying chest X-ray images for pneumonia diagnosis. Five ResNet variants: ResNet18, ResNet34, ResNet50, ResNet101, and ResNet152 are compared on their performance training on the same dataset of chest X-ray images, which included positive and negative diagnoses. The models were trained on a subset of images and tested on a separate set to assess their accuracy and consis
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Pamungkas, Yuri, Evi Triandini, Wawan Yunanto, and Yamin Thwe. "Impact of Hyperparameter Tuning on ResNet-UNet Models for Enhanced Brain Tumor Segmentation in MRI Scans." International Journal of Robotics and Control Systems 5, no. 2 (2025): 917–36. https://doi.org/10.31763/ijrcs.v5i2.1802.

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Brain tumor segmentation in MRI scans is a crucial task in medical imaging, enabling early diagnosis and treatment planning. However, accurately segmenting tumors remains a challenge due to variations in tumor shape, size, and intensity. This study proposes a ResNet-UNet-based segmentation model using LGG dataset (from 110 patients), optimized through hyperparameter tuning to enhance segmentation performance and computational efficiency. The proposed model integrates different ResNet architectures (ResNet18, ResNet34, ResNet50, ResNet101, and ResNet152) with UNet, evaluating their performance
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Guang, Jiahe, Xingrui He, Zeng Li, and Shiyu He. "Road Pothole Detection Model Based on Local Attention Resnet18-CNN-LSTM." Theoretical and Natural Science 42, no. 1 (2024): 131–38. http://dx.doi.org/10.54254/2753-8818/42/20240669.

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Abstract. In response to the low detection accuracy and slow speed of existing road pothole detection methods, a road pothole classification detection model based on local attention Resnet18-CNN-LSTM (Long Short-Term Memory network) is proposed. On the basis of Resnet18, a local attention mechanism and a CNN-LSTM combined model are added to propose a road pothole detection model based on local attention Resnet18-CNN-LSTM. The local attention mechanism is used to accurately extract specific target feature values, CNN is used to extract the spatial features of the input data, and LSTM enhances t
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Xu, Rongman. "Image Classification of Skin Cancer Using Deep Neural Networks with Scaling Laws." International Journal of Computer Science and Information Technology 3, no. 2 (2024): 102–16. http://dx.doi.org/10.62051/ijcsit.v3n2.12.

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Skin cancer image classification is critical to improve healthcare outcomes. Current practice often involves time-consuming procedures that may delay diagnosis until the disease has progressed to an advanced stage, reducing the chances of successful treatment. This challenge is further exacerbated by the worldwide shortage of skilled dermatologists. In this study, we investigate the effect of dataset size on the image classification performance of eight networks (AlexNet, ResNet18, ResNet34, ResNet50, ResNet101, ResNet152, ViT, and MLP-Mixer). We trained these classifiers using different ratio
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Uma Mahesh, RN, HJ Harsha Jain, CS Hemanth Kumar, Umrao Shreyash, and DL Mohith. "Melanocytic Nevi Classification using Transfer Learning." IgMin Research 3, no. 7 (2025): 258–67. https://doi.org/10.61927/igmin307.

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In this paper, the binary classification of skin images has been performed using deep learning technique. i.e the skin disease recognition has been performed using deep learning technique. Here, the binary classification of skin images namely melanocytic nevi and normal skin images has been classified using resnet50 deep learning network. Normal skin images have been considered in TRUE class. Melanocytic nevi skin images have been considered in FALSE class. Traditional method such as biopsy involves lot of computational procedures and consumes a lot of time which is tedious process. Therefore,
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Xing, Xue, Chengzhong Liu, Junying Han, Quan Feng, Qinglin Lu, and Yongqiang Feng. "Wheat-Seed Variety Recognition Based on the GC_DRNet Model." Agriculture 13, no. 11 (2023): 2056. http://dx.doi.org/10.3390/agriculture13112056.

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Wheat is a significant cereal for humans, with diverse varieties. The growth of the wheat industry and the protection of breeding rights can be promoted through the accurate identification of wheat varieties. To recognize wheat seeds quickly and accurately, this paper proposes a convolutional neural network-based image-recognition method for wheat seeds, namely GC_DRNet. The model is based on the ResNet18 network and incorporates the dense network idea by changing its residual module to a dense residual module and introducing a global contextual module, reducing the network model’s parameters
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Saeed, Zubair, Muhammad Haroon Yousaf, Rehan Ahmed, Sergio A. Velastin, and Serestina Viriri. "On-Board Small-Scale Object Detection for Unmanned Aerial Vehicles (UAVs)." Drones 7, no. 5 (2023): 310. http://dx.doi.org/10.3390/drones7050310.

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Object detection is a critical task that becomes difficult when dealing with onboard detection using aerial images and computer vision technique. The main challenges with aerial images are small target sizes, low resolution, occlusion, attitude, and scale variations, which affect the performance of many object detectors. The accuracy of the detection and the efficiency of the inference are always trade-offs. We modified the architecture of CenterNet and used different CNN-based backbones of ResNet18, ResNet34, ResNet50, ResNet101, ResNet152, Res2Net50, Res2Net101, DLA-34, and hourglass14. A co
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Hindarto, Djarot. "COMPARISON OF DETECTION WITH TRANSFER LEARNING ARCHITECTURE RESTNET18, RESTNET50, RESTNET101 ON CORN LEAF DISEASE." Jurnal Teknologi Informasi Universitas Lambung Mangkurat (JTIULM) 8, no. 2 (2023): 41–48. http://dx.doi.org/10.20527/jtiulm.v8i2.174.

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The occurrence of diseases that impact the leaves of corn plants presents a substantial obstacle in agriculture, leading to a reduction in the overall yield of crops. This study aims to perform a comparative analysis of transfer learning methodologies by employing three distinct ResNet architectures: ResNet18, ResNet50, and ResNet101. The dataset utilized by the author consists of a compilation of images portraying corn leaves that demonstrate varying levels of disease severity. Transfer learning refers to leveraging a pre-existing ResNet model and retraining the network by employing the corn
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Wang, Jiayao, Zhen Zhen, Yuting Zhao, Ye Ma, and Yinghui Zhao. "3D-CNN with Multi-Scale Fusion for Tree Crown Segmentation and Species Classification." Remote Sensing 16, no. 23 (2024): 4544. https://doi.org/10.3390/rs16234544.

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Natural secondary forests play a crucial role in global ecological security, climate change mitigation, and biodiversity conservation. However, accurately delineating individual tree crowns and identifying tree species in dense natural secondary forests remains a challenge. This study combines deep learning with traditional image segmentation methods to improve individual tree crown detection and species classification. The approach utilizes hyperspectral, unmanned aerial vehicle laser scanning data, and ground survey data from Maoershan Forest Farm in Heilongjiang Province, China. The study c
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S. Vimala, Anju T. E. ,. "Ensemble Residual Network with Iterative Randomized Hyperparameter Optimization for Colorectal Cancer Classification." Journal of Electrical Systems 20, no. 3s (2024): 01–11. http://dx.doi.org/10.52783/jes.1114.

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The analysis of WSI images is widely acknowledged as a method, for identifying stages of cancer and evaluating the spread of cancer cells in tissues. In histopathology image analysis deep learning models are gaining increasing importance. To enhance the effectiveness of these models it is crucial to train and fine-tune Convolutional Neural Network algorithms by adjusting hyperparameters like batch size, convolution depth, and learning rate (LR). However, determining the hyperparameters can be challenging as they significantly impact model performance. This study examines how hyperparameters in
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Dissertations / Theses on the topic "ResNet18"

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Saeed, Nausheen. "Automated Gravel Road Condition Assessment : A Case Study of Assessing Loose Gravel using Audio Data." Licentiate thesis, Högskolan Dalarna, Institutionen för information och teknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:du-36402.

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Gravel roads connect sparse populations and provide highways for agriculture and the transport of forest goods. Gravel roads are an economical choice where traffic volume is low. In Sweden, 21% of all public roads are state-owned gravel roads, covering over 20,200 km. In addition, there are some 74,000 km of gravel roads and 210,000 km of forest roads that are owned by the private sector. The Swedish Transport Administration (Trafikverket) rates the condition of gravel roads according to the severity of irregularities (e.g. corrugations and potholes), dust, loose gravel, and gravel cross-secti
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Blomqvist, Linus. "Djupinlärning för kameraövervakning." Thesis, Mittuniversitetet, Institutionen för informationssystem och –teknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-40717.

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Allt fler misshandelsbrott sker i Sverige enligt Brå. För att reducera detta kan det som fångats på övervakningskameror användas i brottsutredningar, för att senare användas som bevismaterial till att döma den eller de skyldiga till brottet. Genom att optimera övervakningen kan företag använda sig av automatiserad igenkänning. Automatisering för igenkänningen av normala kontra onormala beteenden går att lösa med djupinlärning. Syftet med denna undersökning är att finna en lämplig modell som kan identifiera det onormala beteendet (till exempel ett slagsmål). Modell arkitekturen som användes und
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Bronda, Samuel. "Hluboké neuronové sítě pro prostředí superpočítače." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2019. http://www.nusl.cz/ntk/nusl-400885.

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The main benefit of the work is the optimization of the hardware configuration for the calculation of neural networks. The theoretical part describes neural networks, deep learning frameworks and hardware options. The next part of the thesis deals with implementation of performance tests, which include application of Inception V3 and ResNet models. Network models are applied to various graphics cards and computing hardware. The output of the thesis is the implemented model of the network Inception V3, which examines the graphics cards and their performance, time-consuming calculations and thei
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Vančo, Timotej. "Self-supervised učení v aplikacích počítačového vidění." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2021. http://www.nusl.cz/ntk/nusl-442510.

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The aim of the diploma thesis is to make research of the self-supervised learning in computer vision applications, then to choose a suitable test task with an extensive data set, apply self-supervised methods and evaluate. The theoretical part of the work is focused on the description of methods in computer vision, a detailed description of neural and convolution networks and an extensive explanation and division of self-supervised methods. Conclusion of the theoretical part is devoted to practical applications of the Self-supervised methods in practice. The practical part of the diploma thesi
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Nguyen, Thanh Tan. "Selected non-convex optimization problems in machine learning." Thesis, Queensland University of Technology, 2020. https://eprints.qut.edu.au/200748/1/Thanh_Nguyen_Thesis.pdf.

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Non-convex optimization is an important and rapidly growing research area. It is tied to the latest success of deep learning, reinforcement learning, matrix factorization, and more. As a contribution to this area, this thesis provides analyses and algorithms for three important problems. The first one is optimization of noisy functions defined on a large graph, which is useful for AB testing, digital marketing. The second one is learning a convex ensemble of basis models, with application in regression and classification. The last one is optimization of ResNet with restricted residual modules,
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D'Amicantonio, Giacomo. "Improvements to knowledge distillation of deep neural networks." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/24178/.

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One of the main problems in the field of Artificial Intelligence is the efficiency of neural networks models. In the past few years, it seemed that most tasks involving such models could simply be solved by designing larger, deeper models and training them on larger datasets for longer time. This approach requires better performing and therefore expensive and energy consuming hardware and will have an increasingly significant environmental impact when those models are deployed at scale. In 2015 G. Hinton, J. Dean and O. Vinyals presented Knowledge Distillation (KD), a technique that leverage
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Kalvakolanu, Anjaneya Teja Sarma. "Brain Tumor Detection and Classification from MRI Images." DigitalCommons@CalPoly, 2021. https://digitalcommons.calpoly.edu/theses/2267.

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A brain tumor is detected and classified by biopsy that is conducted after the brain surgery. Advancement in technology and machine learning techniques could help radiologists in the diagnosis of tumors without any invasive measures. We utilized a deep learning-based approach to detect and classify the tumor into Meningioma, Glioma, Pituitary tumors. We used registration and segmentation-based skull stripping mechanism to remove the skull from the MRI images and the grab cut method to verify whether the skull stripped MRI masks retained the features of the tumor for accurate classification. In
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Asber, Johnny. "A Machine Learning-Based Approach for Fault Detection of Railway Track and its Components." Thesis, Luleå tekniska universitet, Drift, underhåll och akustik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-81275.

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The hard equation of railway safety versus the high commercial profits can only be achieved through the use of new inspection methods supported by modern technologies. The track and its components can have different types of troubles, such as rail surface defects, broken sleepers, missing fasteners, and irregular ballast levels. Each component of the track infrastructure plays a significant role, where the failure or the absence of any of them can pave the way to undesired situations. The rail is designed to carry and direct the train, the sleepers are meant to maintain the level of the rail,
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Zidaoui, Imane. "Advanced data validation methods for wastewater sensors using Artificial Intelligence." Electronic Thesis or Diss., Strasbourg, 2024. http://www.theses.fr/2024STRAD006.

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La fiabilité des données dans la gestion des réseaux d'eaux usées est cruciale en raison des implications directes sur les opérations. Cependant, les approches actuelles de validation des données sont souvent coûteuses et/ou manquent d'objectivité. Cette thèse explore les avancées en intelligence artificielle pour instaurer une validation robuste. La mise en place d'un pôle de validation humaine montre que le F1 score moyen entre experts reste à 0.81, soulignant l'inévitable biais humain. Les modèles testés, à savoir Matrix Profile, ResNet et l'Autoencodeur, présentent des résultats prometteur
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Desai, Gargi Sharad. "Deep Learning for Classification of COVID-19 Pneumonia, Bacterial Pneumonia, Viral Pneumonia and Normal Lungs on CT Images." University of Cincinnati / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1627662447914953.

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Books on the topic "ResNet18"

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ANSI/RESNET/ICC 301-2022 Standard for the Calculation and Labeling of the Energy Performance of Dwelling Units and Sleeping Units Using an Energy Rating Index. International Code Council, 2024.

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Book chapters on the topic "ResNet18"

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Salam, Nader, and T. Jemima Jebaseeli. "Integrating ResNet18 and YOLOv4 for Pedestrian Detection." In Innovations in Computational Intelligence and Computer Vision. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-2602-2_5.

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Kakulapati, V., T. Shirisha, A. Abhiram, B. Sathvik, and V. Tharun. "Cricket Ball Trajectory Prediction and Tracking Using Hybrid Transfer Learning with ResNet50, Alex Net, ResNet18, and Custom CNN." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-0924-6_15.

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Jia, Yongnan, Linjie Dong, Junhua Qi, and Qing Li. "Research on Improving ResNet18 for Classifying Complex Images Based on Attention Mechanism." In Communications in Computer and Information Science. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-3948-6_13.

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Li, Wanting, Xiaoshu Luo, and Ji Chen. "Research on Facial Expression Recognition Method Combined with Improved ResNet18 Network Model." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2259-6_94.

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Katz, Or, Dan Presil, Liz Cohen, Yael Schwartzbard, Sarah Hoch, and Shlomo Kashani. "Pulmonary-Nodule Detection Using an Ensemble of 3D SE-ResNet18 and DPN68 Models." In Lecture Notes in Computer Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-50516-5_33.

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Wang, Ding, Yingnian Wu, Hao Tan, et al. "Application of Improved ResNet18 Based Neural Network for Non-invasive Blood Glucose Testing." In Communications in Computer and Information Science. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-3948-6_1.

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Odusami, Modupe, Rytis Maskeliūnas, Robertas Damaševičius, and Sanjay Misra. "ResD Hybrid Model Based on Resnet18 and Densenet121 for Early Alzheimer Disease Classification." In Intelligent Systems Design and Applications. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-96308-8_27.

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Gayatri, Kowju, and Birendra Biswal. "Classification of Muti-Labeled Retinal Diseases in Retinal Fundus Images Using CNN Model ResNet18." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-97-8336-6_13.

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Ouazzani Chahdi, Meryem, Afafe Annich, Adnane Ouazzani Chahdi, Abdellatif El Abderrahmani, and Khalid Satori. "Advancing Visual Relationship Detection: Comparative Analysis of ResNet101 vs. ResNet152." In Communications in Computer and Information Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-93448-3_27.

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Bekkers, Amerens A., Nina M. van Liebergen, and Hugo J. Kuijf. "Exploring the Use of Off-the-Shelf AI Models for Complex Medical Tasks: ResNet18 for Predicting Age-Related Macular Degeneration." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-86651-7_11.

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Conference papers on the topic "ResNet18"

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Cheng, Shi, Mingze Sun, Zhongyi Huang, Yushan Wang, and Jiaxuan Fu. "Efficient acne classification based on Resnet18." In Third International Conference on Algorithms, Network and Communication Technology (ICANCT 2024), edited by Fabrizio Marozzo. SPIE, 2025. https://doi.org/10.1117/12.3060304.

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Jiang, Xunkai, and Wangke Yu. "Brain tumor classification based on SFFM-ResNet18." In International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2025), edited by Haiquan Zhao and Xinhua Tang. SPIE, 2025. https://doi.org/10.1117/12.3070774.

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Hu, Yixin, Qingyang Ye, Xuanqi Zhu, Mengdan Xing, and Hongqing Zhao. "Traffic sign recognition algorithm based on improved ResNet18." In Fourth International Conference on Computer Graphics, Image and Virtualization (ICCGIV 2024), edited by Hoshang Kolivand and Ata Jahangir Moshayedi. SPIE, 2024. http://dx.doi.org/10.1117/12.3044875.

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Kaushik, Pratham, and Saniya Khurana. "Advancing Oral Health Diagnostics: Superior Disease Classification Using Inception-ResNet-v2 and ResNet18 Deep Learning Models." In 2024 5th IEEE Global Conference for Advancement in Technology (GCAT). IEEE, 2024. https://doi.org/10.1109/gcat62922.2024.10924056.

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Sai, Paladugu Trisha, Gudavalli Hruthi Sri, and T. Lakshmi Surekha. "Sentiment Recognition in Images leveraging ResNet18 vs Vit Architecture." In 2024 Second International Conference on Advances in Information Technology (ICAIT). IEEE, 2024. http://dx.doi.org/10.1109/icait61638.2024.10690800.

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Mao, Yalong, and Tao Wu. "Density detection of knitted fabrics based on improved ResNet18." In Fifth International Conference on Control, Robotics, and Intelligent System (2024), edited by Chenguang Yang. SPIE, 2024. http://dx.doi.org/10.1117/12.3050075.

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Gao, Yingke, Bo Liu, Peidao Wang, and Pei Wang. "Acceleration of ResNet18 Based on Run-time Inference Engine." In 2024 9th International Conference on Integrated Circuits and Microsystems (ICICM). IEEE, 2024. https://doi.org/10.1109/icicm63644.2024.10814151.

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Sar, Ayan, Tanupriya Choudhury, Sumit Aich, et al. "Butterfly Image Classification using Modification and Fine-Tuning of ResNet18." In 2024 OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 4.0. IEEE, 2024. http://dx.doi.org/10.1109/otcon60325.2024.10688302.

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Feng, Xinyun, Tao Peng, Tingting Duan, Haijing Hou, Qiang Chen, and Zhiguo Zhang. "DdbiNet: A dangerous driving behavior identification network based on Resnet18." In 2024 5th International Conference on Information Science, Parallel and Distributed Systems (ISPDS). IEEE, 2024. http://dx.doi.org/10.1109/ispds62779.2024.10667560.

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Wang, Zhangsheng, Yongming Bian, Meng Yang, and Guangjun Liu. "Power Plant Furnace Flame Stability Detection Based on ResNet18-SimAM." In 2024 IEEE Sustainable Power and Energy Conference (iSPEC). IEEE, 2024. https://doi.org/10.1109/ispec59716.2024.10892397.

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Reports on the topic "ResNet18"

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Slone, Scott Michael, Marissa Torres, Nathan Lamie, Samantha Cook, and Lee Perren. Automated change detection in ground-penetrating radar using machine learning in R. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/49442.

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Ground-penetrating radar (GPR) is a useful technique for subsurface change detection but is limited by the need for a subject matter expert to process and interpret coincident profiles. Use of a machine learning model can automate this process to reduce the need for subject matter expert processing and interpretation. Several machine learning models were investigated for the purpose of comparing coincident GPR profiles. Based on our literature review, a Siamese Twin model using a twinned convolutional network was identified as the optimum choice. Two neural networks were tested for the interna
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Yu, Haichao, Haoxiang Li, Honghui Shi, Thomas S. Huang, and Gang Hua. Any-Precision Deep Neural Networks. Web of Open Science, 2020. http://dx.doi.org/10.37686/ejai.v1i1.82.

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We present Any-Precision Deep Neural Networks (Any- Precision DNNs), which are trained with a new method that empowers learned DNNs to be flexible in any numerical precision during inference. The same model in runtime can be flexibly and directly set to different bit-width, by trun- cating the least significant bits, to support dynamic speed and accuracy trade-off. When all layers are set to low- bits, we show that the model achieved accuracy compara- ble to dedicated models trained at the same precision. This nice property facilitates flexible deployment of deep learn- ing models in real-worl
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Taylor, Zachary T., and Vrushali V. Mendon. Identification of RESNET HERS Index Values Corresponding to Minimal Complicance with the IECC Performance Path. Office of Scientific and Technical Information (OSTI), 2014. http://dx.doi.org/10.2172/1133233.

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Taylor, Zachary T., and Supriya Goel. A Preliminary Feasibility Assessment of the RESNET HERS Index as an Alternative Compliance Path for the IECC. Office of Scientific and Technical Information (OSTI), 2013. http://dx.doi.org/10.2172/1133244.

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Identification of RESNET HERS Index Values Corresponding to Minimal Compliance with the IECC Performance Path. Office of Scientific and Technical Information (OSTI), 2014. http://dx.doi.org/10.2172/1764649.

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