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Journal articles on the topic 'ResNet18'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

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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Rajashree, Y. Patil, Gulvani Sampada, B. Waghmare Vishal, and K. Mujawar Irfan. "Image based anthracnose and red-rust leaf disease detection using deep learning." TELKOMNIKA (Telecommunication, Computing, Electronics and Control) 20, no. 6 (2022): 1256–63. https://doi.org/10.12928/telkomnika.v20i6.24262.

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Deep residual learning frameworks have achieved great success in image classification. This article presents the use of transfer learning which is applied on mango leaf image dataset for its disease’s detection. New methodology and training have been used to facilitate the easy and rapid implementation of the mango leaf disease detection system in practice. Proposed system can be used to identify the mango leaf for whether it is healthy or infected with the diseases like anthracnose or red rust. This paper describes all the steps which are considered during the experimentation and design

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Sakaida, Miu, Takaaki Yoshimura, Minghui Tang, Shota Ichikawa, and Hiroyuki Sugimori. "Development of a Mammography Calcification Detection Algorithm Using Deep Learning with Resolution-Preserved Image Patch Division." Algorithms 16, no. 10 (2023): 483. http://dx.doi.org/10.3390/a16100483.

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Convolutional neural networks (CNNs) in deep learning have input pixel limitations, which leads to lost information regarding microcalcification when mammography images are compressed. Segmenting images into patches retains the original resolution when inputting them into the CNN and allows for identifying the location of calcification. This study aimed to develop a mammographic calcification detection method using deep learning by classifying the presence of calcification in the breast. Using publicly available data, 212 mammograms from 81 women were segmented into 224 × 224-pixel patches, pr

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Park, Si Hyeong, Myeong Eon Choi, Seung Yong Lee, Jong-Oh Kim, and No-Suk Park. "A feasibility Study of Detecting Chironomidae Larva in Water Treatment Filtration Processes using ResNet-based Image Recognition Deep Learning." Journal of Korean Society of Environmental Engineers 47, no. 5 (2025): 354–65. https://doi.org/10.4491/ksee.2025.47.5.354.

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There is a possibility that Chironomidae Larva may appear in sand and activated carbon filters during drinking water treatment. This study was conducted to determine whether the presence or absence of larva that may appear in filters through image data analysis. Image data were created for cases with and without larva background with interference materials such as sand and activated carbon granules used in the actual water treatment process. We used ResNet, one of the image classification deep learning models, and verified and evaluated its accuracy. Among the 12 models, the top three models w

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Christin Evasari Nainggolan, Muhammad Nasir, Fatoni, and Devi Udariansyah. "Perbandingan Klasifikasi Jenis Sampah Menggunakan Convolutional Neural Network Dengan Arsitektur ResNet18 dan ResNet50." CSRID (Computer Science Research and Its Development Journal) 16, no. 1 (2024): 76–90. https://doi.org/10.22303/csrid-.16.1.2024.76-90.

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Permasalahan yang kompleks terjadi dalam mengatasi persoalan sampah, baik di negara-negara yang sedang berkembang maupun yang sudah maju, seperti halnya Indonesia. Menurut data dari Kementerian Lingkungan Hidup dan Kehutanan (KLHK), pada tahun 2022 total tumpukan sampah mencapai angka 34.439.338.12 ton per tahun. Dalam penelitian ini akan digunakan machine learning dengan membandingkan arsitektur CNN yaitu ResNet18 dengan ResNet50 untuk klasifikasi jenis-jenis sampah. Penelitian ini menggunakan data citra sampah sebanyak 2527 gambar yang terdiri dari 6 kelas yaitu cardboard, glass, metal, pape

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Sukanya, S. Arockia, and K. Kamalanand. "Deep Learning-Assisted Efficient Staging of SARS-CoV-2 Lesions Using Lung CT Slices." Mathematical Problems in Engineering 2022 (October 19, 2022): 1–12. http://dx.doi.org/10.1155/2022/9613902.

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At present, COVID-19 is a severe infection leading to serious complications. The target site of the SARS-CoV-2 infection is the respiratory tract leading to pneumonia and lung lesions. At present, the severity of the infection is assessed using lung CT images. However, due to the high caseload, it is difficult for radiologists to analyze and stage a large number of CT images every day. Hence, an automated, computer-assisted technique for staging SARS-CoV-2 infection is required. In this work, a comparison of deep learning techniques for the classification and staging of different COVID-19 lung

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Khattak, Afaq, Badr T. Alsulami, and Caroline Mongina Matara. "Hybrid Population Based Training–ResNet Framework for Traffic-Related PM2.5 Concentration Classification." Atmosphere 16, no. 3 (2025): 303. https://doi.org/10.3390/atmos16030303.

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Traffic emissions serve as one of the most significant sources of atmospheric PM2.5 pollution in developing countries, driven by the prevalence of aging vehicle fleets and the inadequacy of regulatory frameworks to mitigate emissions effectively. This study presents a Hybrid Population-Based Training (PBT)–ResNet framework for classifying traffic-related PM2.5 levels into hazardous exposure (HE) and acceptable exposure (AE), based on the World Health Organization (WHO) guidelines. The framework integrates ResNet architectures (ResNet18, ResNet34, and ResNet50) with PBT-driven hyperparameter op

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Yang, Xu, Kejia Ma, Dejia Zhang, Shaozhong Song, and Xiaofeng An. "Classification of soybean seeds based on RGB reconstruction of hyperspectral images." PLOS ONE 19, no. 9 (2024): e0307329. http://dx.doi.org/10.1371/journal.pone.0307329.

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Soyabean is an incredibly significant component of Chinese agricultural product, and categorizing soyabean seeds allows for a better understanding of the features, attributes, and applications of many species of soyabean. This enables farmers to choose appropriate seeds for sowing in order to increase production and quality. As a result, this thesis provides a method for classifying soybean seeds that uses hyperspectral RGB picture reconstruction. Firstly, hyperspectral images of seven varieties of soybean, H1, H2, H3, H4, H5, H6 and H7, were collected by hyperspectral imager, and by using the

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Bai, Jie, Heru Xue, Xinhua Jiang, and Yanqing Zhou. "Classification and recognition of milk somatic cell images based on PolyLoss and PCAM-Reset50." Mathematical Biosciences and Engineering 20, no. 5 (2023): 9423–42. http://dx.doi.org/10.3934/mbe.2023414.

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<abstract> <p>Somatic cell count (SCC) is a fundamental approach for determining the quality of cattle and bovine milk. So far, different classification and recognition methods have been proposed, all with certain limitations. In this study, we introduced a new deep learning tool, i.e., an improved ResNet50 model constructed based on the residual network and fused with the position attention module and channel attention module to extract the feature information more effectively. In this paper, macrophages, lymphocytes, epithelial cells, and neutrophils were assessed. An image datas

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Qayyum, Waqas, Rana Ehtisham, Alireza Bahrami, Charles Camp, Junaid Mir, and Afaq Ahmad. "Assessment of Convolutional Neural Network Pre-Trained Models for Detection and Orientation of Cracks." Materials 16, no. 2 (2023): 826. http://dx.doi.org/10.3390/ma16020826.

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Failure due to cracks is a major structural safety issue for engineering constructions. Human examination is the most common method for detecting crack failure, although it is subjective and time-consuming. Inspection of civil engineering structures must include crack detection and categorization as a key component of the process. Images can automatically be classified using convolutional neural networks (CNNs), a subtype of deep learning (DL). For image categorization, a variety of pre-trained CNN architectures are available. This study assesses seven pre-trained neural networks, including Go

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Ji, Shuaidong. "Multi-classification of Human Action Based on ResNet." Highlights in Science, Engineering and Technology 23 (December 3, 2022): 98–105. http://dx.doi.org/10.54097/hset.v23i.3203.

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With the development of machine learning and other related technologies, more and more excellent research on human action recognition and classification has been proposed, which significantly promotes the application of this technology in the actual situation. This paper mainly focuses on the characteristics of ResNet18, ResNet50, ResNet101, and ResNet152 in 15 human action recognition and classification tasks respectively. First, adjust and enhance the sample images of all training sets and test sets, and adjust the overall parameters according to the characteristics of the input image, so th

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Vinod, Manvika. "Detection of Brain Tumor." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 10 (2023): 1–11. http://dx.doi.org/10.55041/ijsrem26485.

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Brain tumor detection and segmentation are important tasks in medical image analysis. This project is about creating an image classification model to detect whether an MRI image of a brain has a tumor or not. The model is created using Fast ai, which is a high-level deep learning library built on top of Py Torch. The dataset used in this project contains MRI images of brains with and without tumors. The model is trained using transfer learning with ResNet18 and ResNet34 as the base architectures. After training the model, it is exported and used to make predictions on new images using a simple

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Wang, Lulu. "Holographic Microwave Image Classification Using a Convolutional Neural Network." Micromachines 13, no. 12 (2022): 2049. http://dx.doi.org/10.3390/mi13122049.

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Holographic microwave imaging (HMI) has been proposed for early breast cancer diagnosis. Automatically classifying benign and malignant tumors in microwave images is challenging. Convolutional neural networks (CNN) have demonstrated excellent image classification and tumor detection performance. This study investigates the feasibility of using the CNN architecture to identify and classify HMI images. A modified AlexNet with transfer learning was investigated to automatically identify, classify, and quantify four and five different HMI breast images. Various pre-trained networks, including ResN

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Sanga, S. L., D. Machuve, and K. Jomanga. "Mobile-based Deep Learning Models for Banana Disease Detection." Engineering, Technology & Applied Science Research 10, no. 3 (2020): 5674–77. http://dx.doi.org/10.48084/etasr.3452.

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In Tanzania, smallholder farmers contribute significantly to banana production and Kagera, Mbeya, and Arusha are among the leading regions. However, pests and diseases are a threat to food security. Early detection of banana diseases is important to identify the diseases before too much damage is done on the plants. In this paper, a tool for early detection of banana diseases by using a deep learning approach is proposed. Five deep learning architectures, namely Vgg16, Resnet18, Resnet50, Resnet152 and InceptionV3 were used to develop models for banana disease detection, achieving all high acc

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Sanga, S. L., D. Machuve, and K. Jomanga. "Mobile-based Deep Learning Models for Banana Disease Detection." Engineering, Technology & Applied Science Research 10, no. 3 (2020): 5674–77. https://doi.org/10.5281/zenodo.3934580.

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In Tanzania, smallholder farmers contribute significantly to banana production and Kagera, Mbeya, and Arusha are among the leading regions. However, pests and diseases are a threat to food security. Early detection of banana diseases is important to identify the diseases before too much damage is done on the plants. In this paper, a tool for early detection of banana diseases by using a deep learning approach is proposed. Five deep learning architectures, namely Vgg16, Resnet18, Resnet50, Resnet152 and InceptionV3 were used to develop models for banana disease detection, achieving all high acc

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Kusumawardani, Rindi, and Putu Dana Karningsih. "Detection and Classification of Canned Packaging Defects Using Convolutional Neural Network." PROZIMA (Productivity, Optimization and Manufacturing System Engineering) 4, no. 1 (2021): 1–11. http://dx.doi.org/10.21070/prozima.v4i1.1280.

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Packaging is one of the important aspects of a product’s identity. The good and adorable packaging can increase product competitiveness because it gives a perception to the customers of good quality products. Therefore, a good packaging display is necessary so that packaging quality inspection is very important. Automated defect detection can help to reduce human error in the inspection process. Convolutional Neural Network (CNN) is an approach that can be used to detect and classify a packaging condition. This paper presents an experiment that compares 5 network models, i.e. ShuffleNet, GoogL

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Ullah, Naeem, Javed Ali Khan, Mohammad Sohail Khan, et al. "An Effective Approach to Detect and Identify Brain Tumors Using Transfer Learning." Applied Sciences 12, no. 11 (2022): 5645. http://dx.doi.org/10.3390/app12115645.

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Brain tumors are considered one of the most serious, prominent and life-threatening diseases globally. Brain tumors cause thousands of deaths every year around the globe because of the rapid growth of tumor cells. Therefore, timely analysis and automatic detection of brain tumors are required to save the lives of thousands of people around the globe. Recently, deep transfer learning (TL) approaches are most widely used to detect and classify the three most prominent types of brain tumors, i.e., glioma, meningioma and pituitary. For this purpose, we employ state-of-the-art pre-trained TL techni

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Mulyawan, Rifqi, Andi Sunyoto, and Alva Hendi Muhammad Muhammad. "Pre-Trained CNN Architecture Analysis for Transformer-Based Indonesian Image Caption Generation Model." JOIV : International Journal on Informatics Visualization 7, no. 2 (2023): 487. http://dx.doi.org/10.30630/joiv.7.2.1387.

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Classification and object recognition in image processing has significantly improved computer vision tasks. The method is often used for visual problems, especially in picture classification utilizing the Convolutional Neural Network (CNN). In the popular state-of-the-art (SOTA) task of generating a caption on an image, the implementation is often used for feature extraction of an image as an encoder. Instead of performing direct classification, these extracted features are sent from the encoder to the decoder section to generate the sequence. So, some CNN layers related to the classification

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Huang, Yingcong, Kunal Chaturvedi, Al-Akhir Nayan, Mohammad Hesam Hesamian, Ali Braytee, and Mukesh Prasad. "Early Parkinson’s Disease Diagnosis through Hand-Drawn Spiral and Wave Analysis Using Deep Learning Techniques." Information 15, no. 4 (2024): 220. http://dx.doi.org/10.3390/info15040220.

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Parkinson’s disease (PD) is a chronic brain disorder affecting millions worldwide. It occurs when brain cells that produce dopamine, a chemical controlling movement, die or become damaged. This leads to PD, which causes problems with movement, balance, and posture. Early detection is crucial to slow its progression and improve the quality of life for PD patients. This paper proposes a handwriting-based prediction approach combining a cosine annealing scheduler with deep transfer learning. It utilizes the NIATS dataset, which contains handwriting samples from individuals with and without PD, to

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Pornpanomchai, Chomtip. "Image Based Papaya (Carica Papaya Linn.) seed germination evaluation by ResNet50." ASEAN Journal of Scientific and Technological Reports 28, no. 1 (2025): e254804. https://doi.org/10.55164/ajstr.v28i1.254804.

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The researchers developed a papaya seed germination evaluation system (PSGES) using ResNet50, a convolutional neural network, to evaluate papaya seed germination potential from single seed images. Using a comprehensive dataset of 12,600 papaya seed images, they allocated 11,600 images for training (with an 80/20 training-testing split) and 1,000 images for validation. The system achieved impressive performance metrics, with an overall accuracy of 99.58% and an average processing time of 1.4705 seconds per image. The training dataset demonstrated exceptional performance with 0.9958 accuracy, 0.

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Zhang, Jian, Jingwei Yang, Ting An, et al. "AFC-ResNet18: A Novel Real-Time Image Semantic Segmentation Network for Orchard Scene Understanding." Journal of the ASABE 67, no. 2 (2024): 493–500. http://dx.doi.org/10.13031/ja.15682.

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Highlights A novel real-time image semantic segmentation network for orchards, termed AFC-ResNet18, was designed and tested. The AFC-ResNet18 model outperformed the SwiftNet network in terms of segmentation depth. The AFC-ResNet18 model achieved the highest accuracy in the architecture performance testing. The AFC-ResNet18 model won first place in the orchard scene test with 72.5% accuracy. Abstract. Semantic segmentation is a fundamental prerequisite for the real-time understanding of scenes. This understanding is essential for developing automated devices that can enhance productivity. Orcha

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Naveen, Chinta Venkata, Gangishetti Abhiram, V. Aneesh, V. Kakulapati, and Kranthi Kumar. "Monkeypox Detection Using Transfer Learning, ResNet50, Alex Net, ResNet18 & Custom CNN Model." Asian Journal of Advanced Research and Reports 17, no. 5 (2023): 7–13. http://dx.doi.org/10.9734/ajarr/2023/v17i5480.

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The latest monkeypox flare-up has arisen as a general well-being worry because of the fast spread to more than 40 countries that are not situated in Africa. Because of likenesses with chickenpox and measles, the early clinical ID of monkeypox can challenge. PC helped recognition of monkeypox sores might be valuable for checking and fast recognizable proof of thought situations when corroborative Polymerase Chain Reaction (PCR) tests are inaccessible. At the point when there are sufficient preparation models, profound learning strategies have been demonstrated to be helpful for naturally distin

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Shukhaev, S. V., E. A. Mordovtseva, E. A. Pustozerov, and S. S. Kudlakhmedov. "Application of convolutional neural networks to define Fuchs endothelial dystrophy." Fyodorov journal of ophthalmic surgery, no. 1S (February 17, 2023): 70–76. http://dx.doi.org/10.25276/0235-4160-2022-4s-70-76.

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Purpose. To evaluate the application of convolutional neural networks for the automatic detection of Fuchs' dystrophy. Material and methods. The study included 700 biomicroscopic images of the corneal endothelium (Tomey EM-3000) randomly selected from the database of the Saint-Petersburg brunch of the S. Fyodorov Eye Microsurgery Federal State Institution. At the first stage, the images were divided into 2 groups. The first group included images with the presence of Fuchs' dystrophy, the second – another pathology or a healthy cornea. The corneal endothelial cell density images were divided in

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Czarnecki, Joby M. Prince, Sathishkumar Samiappan, Meilun Zhou, Cary Daniel McCraine, and Louis L. Wasson. "Real-Time Automated Classification of Sky Conditions Using Deep Learning and Edge Computing." Remote Sensing 13, no. 19 (2021): 3859. http://dx.doi.org/10.3390/rs13193859.

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The radiometric quality of remotely sensed imagery is crucial for precision agriculture applications because estimations of plant health rely on the underlying quality. Sky conditions, and specifically shadowing from clouds, are critical determinants in the quality of images that can be obtained from low-altitude sensing platforms. In this work, we first compare common deep learning approaches to classify sky conditions with regard to cloud shadows in agricultural fields using a visible spectrum camera. We then develop an artificial-intelligence-based edge computing system to fully automate th

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Wagle, Shivali Amit, Harikrishnan R, Jahariah Sampe, Faseehuddin Mohammad, and Sawal Hamid Md Ali. "Effect of Data Augmentation in the Classification and Validation of Tomato Plant Disease with Deep Learning Methods." Traitement du Signal 38, no. 6 (2021): 1657–70. http://dx.doi.org/10.18280/ts.380609.

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The paper discusses disease identification and classification in tomato plants, as well as the effect of data augmentation in deep learning models. The database used here is Tomato plant leaves (TPL) images from the PlantVillage Database in the healthy and disease classes. The disease categories have been chosen depending on their occurrence in the Indian States. The proposed ResNet50, ResNet18, and ResNet101 deep-learning model with transfer learning combined with the softmax classification are used to identify and categorize the tomato leaf images into the healthy or diseases classes in the

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Sukmana, Septian Enggar, Deasy Sandhya Elya Ikawati, Habibie Ed Dien, and Ashafidz Fauzan Dianta. "Penentuan Learning Rate Terbaik CNN Pada Pengenalan Individu Berbasis Analisis Gait." JOINS (Journal of Information System) 8, no. 1 (2023): 90–96. http://dx.doi.org/10.33633/joins.v8i1.7806.

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Trayektori tubuh manusia untuk analisis gait tidak terbatas pada kondisi permukaan medan yang rata. Hal ini berpengaruh pada analisis gait untuk penelitian pengenalan identitas individu yang terkait dengan kondisi medan yang dilalui. Pergelangan kaki menjadi bagian tubuh yang berkontribusi pada trayektori tubuh manusia terhadap medan yang dilalui melalui dua kondisi yaitu Heel-Strike (HS) dan Toe-Off (TO). HS dan TO memiliki pola trayektori yang saling berbeda untuk setiap individu sehingga membutuhkan penentuan parameter learning rate yang tepat. Penentuan learning rate terbaik merupakan sala

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Liu, Fangzhou, Wenzhe Zhao, Haoxiang Qi, and Guangyao Zhou. "SIGKD: A Structured Instance Graph Distillation Method for Efficient Object Detection in Remote Sensing Images." Remote Sensing 16, no. 23 (2024): 4443. http://dx.doi.org/10.3390/rs16234443.

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In the realm of remote sensing image object detection compression distillation, establishing an efficient method for instance feature knowledge transfer between teacher and student models holds paramount importance. To this end, this paper introduces an innovative deep structured instance graph distillation method that endeavors to delve into the underlying information between instance features, thereby optimizing detection performance. Specifically, our proposed method incorporates feature instances and their relations into a graph-based structure (SIG). In this graph, feature instances serve

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Witkowski, Konrad, and Mikołaj Wieczorek. "USAGE OF ARTIFICIAL NEURAL NETWORKS IN THE DIAGNOSIS OF KNEE JOINT DISORDERS." Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska 13, no. 4 (2023): 11–14. http://dx.doi.org/10.35784/iapgos.5380.

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Following article address the issue of automatic knee disorder diagnose with usage of neural networks. We proposed several hybrid neural net architectures which aim to successfully classify abnormality using MRI (magnetic resonance imaging) images acquired from publicly available dataset. To construct such combinations of models we used pretrained Alexnet, Resnet18 and Resnet34 downloaded from Torchvision. Experiments showed that for certain abnormalities our models can achieve up to 90% accuracy.

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Chen, Yuzhi. "Application of Resnet18-Unet in separating tumors from brain MRI images." Journal of Physics: Conference Series 2580, no. 1 (2023): 012057. http://dx.doi.org/10.1088/1742-6596/2580/1/012057.

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Abstract The automatic segmentation of brain tumors in magnetic resonance imaging (MRI) plays a significant role in diagnosis and treatment planning. In this research, a semantic segmentation model called Resnet18-Unet was designed to combine the Resnet18 model and the U-Net model by using the convolutional neural network’s powerful feature extraction functionality. It keeps the decoding part of U-Net and replaces the traditional VGG encoding part with Resnet18 to extract feature information more effectively. Aiming at the problem of slow training speed and difficult convergence of semantic se

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Ma, Suqiang, Chun Liu, Zheng Li, and Wei Yang. "Integrating Adversarial Generative Network with Variational Autoencoders towards Cross-Modal Alignment for Zero-Shot Remote Sensing Image Scene Classification." Remote Sensing 14, no. 18 (2022): 4533. http://dx.doi.org/10.3390/rs14184533.

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Remote sensing image scene classification takes image blocks as classification units and predicts their semantic descriptors. Because it is difficult to obtain enough labeled samples for all classes of remote sensing image scenes, zero-shot classification methods which can recognize image scenes that are not seen in the training stage are of great significance. By projecting the image visual features and the class semantic features into the latent space and ensuring their alignment, the variational autoencoder (VAE) generative model has been applied to address remote-sensing image scene classi

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Cui, Hanzhi, Dawei Huang, Wancheng Feng, Zhengao Li, Qiuxue Ouyang, and Conghan Zhong. "FIAEPI-KD: A novel knowledge distillation approach for precise detection of missing insulators in transmission lines." PLOS One 20, no. 5 (2025): e0324524. https://doi.org/10.1371/journal.pone.0324524.

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Ensuring transmission line safety is crucial. Detecting insulator defects is a key task. UAV-based insulator detection faces challenges: complex backgrounds, scale variations, and high computational costs. To address these, we propose FIAEPI-KD, a knowledge distillation framework integrating Feature Indicator Attention (FIA) and Edge Preservation Index (EPI). The method employs ResNet and FPN for multi-scale feature extraction. The FIA module dynamically focuses on multi-scale insulator edges via dual-path attention mechanisms, suppressing background interference. The EPI module quantifies edg

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Habeeb, Mustafa Abdulfattah, Yahya Layth Khaleel, Reem D. Ismail, Z. T. Al-Qaysi, and Fatimah N. Ameen. "Deep Learning Approaches for Gender Classification from Facial Images." Mesopotamian Journal of Big Data 2024 (October 11, 2024): 185–98. http://dx.doi.org/10.58496/mjbd/2024/013.

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Gender recognition on the facial level is considered one of the most important technologies that finds use in such fields as a personalized marketing plan, safe systems of authentication, and effective human-computer interfaces. However, it has the following challenges; variation of lighting, facial movement, and ethnic/age face images. AI and DL has been improving on the effectiveness, flexibility, and speed of the gender classification system. AI enables complex and automatic feature learning in Data, while DL is tailored for handle variants in vision-based data. In this paper, we evaluated

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Ozcelik, Salih T. A., Hakan Uyanık, Erkan Deniz, and Abdulkadir Sengur. "Automated Hypertension Detection Using ConvMixer and Spectrogram Techniques with Ballistocardiograph Signals." Diagnostics 13, no. 2 (2023): 182. http://dx.doi.org/10.3390/diagnostics13020182.

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Blood pressure is the pressure exerted by the blood in the veins against the walls of the veins. If this value is above normal levels, it is known as high blood pressure (HBP) or hypertension (HPT). This health problem which often referred to as the “silent killer” reduces the quality of life and causes severe damage to many body parts in various ways. Besides, its mortality rate is very high. Hence, rapid and effective diagnosis of this health problem is crucial. In this study, an automatic diagnosis of HPT has been proposed using ballistocardiography (BCG) signals. The BCG signals were trans

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Almisreb, Ali Abd, Nooritawati Md Tahir, Sherzod Turaev, Mohammed A. Saleh, and Syed Abdul Mutalib Al Junid. "Arabic Handwriting Classification using Deep Transfer Learning Techniques." Pertanika Journal of Science and Technology 30, no. 1 (2022): 641–54. http://dx.doi.org/10.47836/pjst.30.1.35.

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Arabic handwriting is slightly different from the handwriting of other languages; hence it is possible to distinguish the handwriting written by the native or non-native writer based on their handwriting. However, classifying Arabic handwriting is challenging using traditional text recognition algorithms. Thus, this study evaluated and validated the utilisation of deep transfer learning models to overcome such issues. Hence, seven types of deep learning transfer models, namely the AlexNet, GoogleNet, ResNet18, ResNet50, ResNet101, VGG16, and VGG19, were used to determine the most suitable mode

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Han, Lu, Chongchong Yu, Kaitai Xiao, and Xia Zhao. "A New Method of Mixed Gas Identification Based on a Convolutional Neural Network for Time Series Classification." Sensors 19, no. 9 (2019): 1960. http://dx.doi.org/10.3390/s19091960.

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This paper proposes a new method of mixed gas identification based on a convolutional neural network for time series classification. In view of the superiority of convolutional neural networks in the field of computer vision, we applied the concept to the classification of five mixed gas time series data collected by an array of eight MOX gas sensors. Existing convolutional neural networks are mostly used for processing visual data, and are rarely used in gas data classification and have great limitations. Therefore, the idea of mapping time series data into an analogous-image matrix data is p

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Wang, Jianping, Youchao Wang, Boyan Chen, Xiaoyue Jia, and Dexi Pu. "Trajectory Classification and Recognition of Planar Mechanisms Based on ResNet18 Network." Algorithms 17, no. 8 (2024): 324. http://dx.doi.org/10.3390/a17080324.

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This study utilizes the ResNet18 network to classify and recognize trajectories of planar mechanisms. This research begins by deriving formulas for trajectory points in various typical planar mechanisms, and the resulting trajectory images are employed as samples for training and testing the network. The classification of trajectory images for both upright and inverted configurations of a planar four-bar linkage is investigated. Compared with AlexNet and VGG16, the ResNet18 model demonstrates superior classification accuracy during testing, coupled with reduced training time and memory consump

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Yang, Zhikang, Mao Shi, Yassine Gharbi, et al. "A Near-Infrared Imaging System for Robotic Venous Blood Collection." Sensors 24, no. 22 (2024): 7413. http://dx.doi.org/10.3390/s24227413.

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Venous blood collection is a widely used medical diagnostic technique, and with rapid advancements in robotics, robotic venous blood collection has the potential to replace traditional manual methods. The success of this robotic approach is heavily dependent on the quality of vein imaging. In this paper, we develop a vein imaging device based on the simulation analysis of vein imaging parameters and propose a U-Net+ResNet18 neural network for vein image segmentation. The U-Net+ResNet18 neural network integrates the residual blocks from ResNet18 into the encoder of the U-Net to form a new neura

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Qu, Tan, Zhiming Zhao, Yan Zhang, Jiaji Wu, and Zhensen Wu. "Mode Recognition of Orbital Angular Momentum Based on Attention Pyramid Convolutional Neural Network." Remote Sensing 14, no. 18 (2022): 4618. http://dx.doi.org/10.3390/rs14184618.

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In an effort to address the problem of the insufficient accuracy of existing orbital angular momentum (OAM) detection systems for vortex optical communication, an OAM mode detection technology based on an attention pyramid convolution neural network (AP-CNN) is proposed. By introducing fine-grained image classification, the low-level detailed features of the similar light intensity distribution of vortex beam superposition and plane wave interferograms are fully utilized. Using ResNet18 as the backbone of AP-CNN, a dual path structure with an attention pyramid is adopted to detect subtle diffe

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Yogamadhavan, Vicknesh Kumar, and Dr Giriraj Mannayee. "An Evaluation of Various Deep Convolutional Networks for the Development of a Vision System for the Classification of Domestic Solid Street Waste." Journal of Internet Services and Information Security 14, no. 2 (2024): 268–83. http://dx.doi.org/10.58346/jisis.2024.i2.017.

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Artificial intelligence is an emerging technology revolutionizing the modern world and making the life of mankind easier and more efficient. Its application in various fields is growing every moment like tributaries of flooded rivers. To apply Artificial Intelligence in the field of domestic waste collection, the images of 7 various categories of waste such as cardboards and tetra packs, dairy packets, facemasks, footwear, paper cups, plastic bottles, and wrappers taken in the various factual street environments under natural lighting were trained and tested through different pre-trained convo

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Yang, Shuang, Lingzhi Xue, Xi Hong, and Xiangyang Zeng. "A Lightweight Network Model Based on an Attention Mechanism for Ship-Radiated Noise Classification." Journal of Marine Science and Engineering 11, no. 2 (2023): 432. http://dx.doi.org/10.3390/jmse11020432.

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Recently, deep learning has been widely used in ship-radiated noise classification. To improve classification efficiency, avoiding high computational costs is an important research direction in ship-radiated noise classification. We propose a lightweight squeeze and excitation residual network 10 (LW-SEResNet10). In ablation experiments of LW-SEResNet10, the use of ResNet10 instead of ResNet18 reduced 56.1% of parameters, while the accuracy is equivalent to ResNet18. The improved accuracy indicates that the ReLU6 enhanced the model stability, and an attention mechanism captured the channel dep

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Nirgude, Vaishali, and Sheetal Rathi. "A ROBUST DEEP LEARNING APPROACH TO ENHANCE THE ACCURACY OF POMEGRANATE FRUIT DISEASE DETECTION UNDER REAL FIELD CONDITION." Journal of Experimental Biology and Agricultural Sciences 9, no. 6 (2021): 863–70. http://dx.doi.org/10.18006/2021.9(6).863.870.

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Pomegranate fruits are infected by various diseases and pests, which negatively affect food security, productivity, and quality. Recent advancements in deep learning with Convolutional Neural Networks (CNNs) have significantly improved the accuracy of fruit disease detection and classification. The main objective of this investigation is to find the most suitable deep-learning architecture to enhance fruit disease detection and classification accuracy. The current study proposed an efficient deep learning-based approach to detect the most prominent diseases of pomegranate such as bacterial bli

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