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Journal articles on the topic 'Modified VGG-16'

Author: Grafiati

Published: 5 June 2025

Last updated: 1 August 2025

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1

劉怡, 劉怡. "Research of Art Point of Interest Recommendation Algorithm Based on Modified VGG-16 Network." 電腦學刊 33, no. 1 (2022): 071–85. http://dx.doi.org/10.53106/199115992022023301008.

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<p>Traditional point of interest (POI) recommendation algorithms ignore the semantic context of comment information. Integrating convolutional neural networks into recommendation systems has become one of the hotspots in art POI recommendation research area. To solve the above problems, this paper proposes a new art POI recommendation model based on improved VGG-16. Based on the original VGG-16, the improved VGG-16 method optimizes the fully connection layer and uses transfer learning to share the weight parameters of each layer in VGG-16 pre-training model for subsequent training. The n

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Abhinandan, Kalita. "Detection of COVID-19 using Modified VGG Architectures." International Journal of Current Science Research and Review 05, no. 06 (2022): 2113–18. https://doi.org/10.5281/zenodo.6685614.

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<strong>ABSTRACT: </strong>COVID-19 has created havoc in the world. This paper aims to study and understand the performance of modified VGG-16 and VGG-19 architectures in detecting COVID-19 using the concept of transfer learning. The algorithm has been validated using a private dataset with normal and COVID-19 positive chest X-ray images.  

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Xiao, Zhitao, Mandi Wang, Lei Geng, Jun Wu, Fang Zhang, and Chunyan Shan. "Optic Cup Segmentation Method by a Modified VGG-16 Network." Journal of Medical Imaging and Health Informatics 9, no. 1 (2019): 97–101. http://dx.doi.org/10.1166/jmihi.2019.2546.

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Reddi, Prasadu, Gorla Srinivas, P. V. G. D. Prasad Reddy, and Dasari Siva Krihsna. "A Multi-Head Self-Attention Mechanism for Improved Brain Tumor Classification using Deep Learning Approaches." Engineering, Technology & Applied Science Research 14, no. 5 (2024): 17324–29. http://dx.doi.org/10.48084/etasr.8484.

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One of the most common life-threatening diseases, the brain tumor is a condition characterized by the rapid proliferation of abnormal cells that leads to the destruction of healthy brain cells. Its aggressive nature can result in a patient succumbing to the disease before an accurate diagnosis is achieved. Timely detection is crucial to effective treatment and patient survival. Similarly, early detection plays a pivotal role in the case of brain tumors, where swift identification is vital to providing optimal care and increasing the chances of patient recovery. Streamlining the complex process

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Dawood, Afrah Salman. "A Comparative Study of DCNN Models and Transfer Learning Effect for Sustainability Assessment: The Case of Garbage Classification." Technium: Romanian Journal of Applied Sciences and Technology 12 (August 1, 2023): 33–44. http://dx.doi.org/10.47577/technium.v12i.9346.

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Recently, with the large development of AI, ML and DL with a wide range of different fields includes sustainability and environmental applications. Sustainability has three major pillars which are environment, economy and society in order to keep all systems balanced on earth for a larger number of generations. In this research, two modified DCNN models were implemented and tested for predicting and classifying garbage images into six types of garbage according to trashNet dataset. These models are CNN and VGG-16 and are implemented according to transfer learning aspect. Both models used in th

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Le, Nhat Anh, Jucheol Moon, Christopher G. Lowe, Hyun-Il Kim, and Sang-Il Choi. "An Automated Framework Based on Deep Learning for Shark Recognition." Journal of Marine Science and Engineering 10, no. 7 (2022): 942. http://dx.doi.org/10.3390/jmse10070942.

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The recent progress in deep learning has given rise to a non-invasive and effective approach for animal biometrics. These modern techniques allow researchers to track animal individuals on a large-scale image database. Typical approaches are suited to a closed-set recognition problem, which is to identify images of known objects only. However, such approaches are not scalable because they mis-classify images of unknown objects. To recognize the images of unknown objects as ‘unknown’, a framework should be able to deal with the open set recognition scenario. This paper proposes a fully automati

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Shah, Syed Rehan, Salman Qadri, Hadia Bibi, Syed Muhammad Waqas Shah, Muhammad Imran Sharif, and Francesco Marinello. "Comparing Inception V3, VGG 16, VGG 19, CNN, and ResNet 50: A Case Study on Early Detection of a Rice Disease." Agronomy 13, no. 6 (2023): 1633. http://dx.doi.org/10.3390/agronomy13061633.

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Rice production has faced numerous challenges in recent years, and traditional methods are still being used to detect rice diseases. This research project developed an automated rice blast disease diagnosis technique based on deep learning, image processing, and transfer learning with pre-trained models such as Inception V3, VGG16, VGG19, and ResNet50. The public dataset consists of 2000 images; about 1200 images belong to the leaf blast class, and 800 to the healthy leaf class. The modified connection-skipping ResNet 50 had the highest accuracy of 99.75% with a loss rate of 0.33, while the ot

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Haq, Ibnu Kasyful, and Agi Prasetiadi. "MAKHRAJ ‘AIN PRONUNCIATION ERROR DETECTION USING MEL FREQUENCY CEPSTRAL COEFFICIENT AND MODIFIED VGG-16." Jurnal Teknik Informatika (Jutif) 4, no. 1 (2023): 217–24. http://dx.doi.org/10.52436/1.jutif.2023.4.1.419.

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Based on research conducted by the Institute of Qur'anic Sciences (IIQ) as many as 65% of Muslims in Indonesia are illiterate in the Qur'an. In previous studies, research was conducted on the detection of Arabic word pronunciation errors against non-natives using the Mel Frequency Cepstral Coefficient (MFCC) and Support Vector Machine (SVM) methods with a test result of 54.6%. Due to the low accuracy results in previous studies, this study aims to design and build a system that can correct the accuracy of the pronunciation of makhraj letter ‘ain with the method used is a combination of MFCC an

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Sridevi, C., and M. Kannan. "Convolutional Neural Network Architecture-Inception (GoogLeNet) For Deep Architected Learning-Assisted Lung Cancer Classification in Computed Tomography Images." Indian Journal Of Science And Technology 18, no. 7 (2025): 504–16. https://doi.org/10.17485/ijst/v18i7.3086.

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Objectives: The primary objective of this study is to investigate the performance of AlexNet and GoogLeNet architectures in classifying lung cancer images from the LIDC-IDRI dataset. Additionally, the AlexNet architecture is modified to generate three classes (benign, malignant, and non-nodules) using a new dataset. Methods: This study utilizes the LIDC-IDRI dataset, consisting of 1018 thoracic CT scans from 1010 patient cases, with annotations from four radiologists. The AlexNet and GoogLeNet architectures are employed for image classification, with the following Hyperparameters: AlexNet uses

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C, Sridevi, and Kannan M. "Convolutional Neural Network Architecture-Inception (GoogLeNet) For Deep Architected Learning-Assisted Lung Cancer Classification in Computed Tomography Images." Indian Journal of Science and Technology 18, no. 7 (2025): 504–16. https://doi.org/10.17485/IJST/v18i7.3086.

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<strong>Objectives:</strong> The primary objective of this study is to investigate the performance of AlexNet and GoogLeNet architectures in classifying lung cancer images from the LIDC-IDRI dataset. Additionally, the AlexNet architecture is modified to generate three classes (benign, malignant, and non-nodules) using a new dataset. <strong>Methods:</strong> This study utilizes the LIDC-IDRI dataset, consisting of 1018 thoracic CT scans from 1010 patient cases, with annotations from four radiologists. The AlexNet and GoogLeNet architectures are employed for image classification,

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Akram, Noreen, Rizwan Aslam Butt, and Muhammad Amir Qureshi. "Modification of a convolutional neural network for the weave pattern classification." Mehran University Research Journal of Engineering and Technology 43, no. 2 (2024): 79. http://dx.doi.org/10.22581/muet1982.2998.

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The fabric quality in textile industry is characterized by the texture (weave pattern) as it plays a vital role for the production and design of best quality fabric. The earlier proposed automated weave identification methods based on image processing techniques are highly dependent on the lighting conditions. The machine learning methods have been reported to show better accuracy. However, they require very large training datasets, very high processing power and computation time. This study proposes improved accuracy with smaller dataset and reduced computation time by proposing a modificatio

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Hu, Junping, Shitu Abubakar, Shengjun Liu, Xiaobiao Dai, Gen Yang, and Hao Sha. "Near-Infrared Road-Marking Detection Based on a Modified Faster Regional Convolutional Neural Network." Journal of Sensors 2019 (December 27, 2019): 1–11. http://dx.doi.org/10.1155/2019/7174602.

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Pedestrians, motorist, and cyclist remain the victims of poor vision and negligence of human drivers, especially in the night. Millions of people die or sustain physical injury yearly as a result of traffic accidents. Detection and recognition of road markings play a vital role in many applications such as traffic surveillance and autonomous driving. In this study, we have trained a nighttime road-marking detection model using NIR camera images. We have modified the VGG-16 base network of the state-of-the-art faster R-CNN algorithm by using a multilayer feature fusion technique. We have demons

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Idhom, Mohammad, Dwi Arman Prasetya, Prismahardi Aji Riyantoko, Tresna Maulana Fahrudin, and Anggraini Puspita Sari. "Pneumonia Classification Utilizing VGG-16 Architecture and Convolutional Neural Network Algorithm for Imbalanced Datasets." TIERS Information Technology Journal 4, no. 1 (2023): 73–82. http://dx.doi.org/10.38043/tiers.v4i1.4380.

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This research focuses on accurately classifying pneumonia in children under the age of 5 using X-ray images, considering the challenge of an imbalanced dataset. A modified VGG-16 CNN architecture is evaluated for pneumonia classification in Chest X-Ray Images. The study compares testing results with and without data augmentation techniques and explores the potential application of the model in an Android-based machine learning system for pneumonia diagnosis assistance. Using a dataset of 5,856 Chest X-Ray images categorized as normal or pneumonia, obtained from Kaggle, the research conducts tw

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Raorane, Ashwini, Dhiraj Magare, and Yogita Mistry. "A novel technique for implementing hybrid optimization technique for PV thermal images to categorize and localize the faults." Intelligent Decision Technologies 18, no. 1 (2024): 169–89. http://dx.doi.org/10.3233/idt-230631.

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In order to identify and locate flaws in solar thermal images, this research suggests using an optimization-tuned CNN classifier. The input thermal images are initially pre-processed to remove the noise present in them. After pre-processing, features like LBP, LDP, and LOOP are extracted. The collected features are then combined to produce a feature vector, which is the input to the proposed CNN classifier. Single hotspots, multiple hotspots, and string hotspots are the three types of faults that are supposed to be classified. After the classification process, the defects are located using the

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Seidaliyeva, U. O., and L. B. Ilipbayeva. "CLASSIFICATION OF FLYING OBJECTS USING A MODIFIED MODEL OF VGG-16 CNN BASED ON VISUAL DATA." Вестник Алматинского университета энергетики и связи, no. 4 (2020): 70–77. http://dx.doi.org/10.51775/1999-9801_2020_51_4_70.

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Kareem, Omar Sedqi, and Ahmed Khorsheed Al-Sulaifanie. "Classification of COVID-19 Cases from X-Ray Images Based on a Modified VGG-16 Model." Traitement du Signal 39, no. 1 (2022): 255–63. http://dx.doi.org/10.18280/ts.390126.

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COVID-19 is considered one of the most deadly pandemics by the World Health Organization and has claimed the lives of millions around the world. Mechanisms for early diagnosis and detection of this rapidly spreading disease are necessary to save lives. However, the increase in COVID-19 cases requires not relying on traditional means of detecting diseases due to these tests’ limitations and high costs. One diagnostic technique for COVID-19 is X-rays and CT scans. For accurate and highly efficient diagnosis, computer-aided diagnosis is required. In this research, we suggest a convolutional neura

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Liu, Kevin. "Comparison of different Convolutional Neural Network models on Fruit 360 Dataset." Highlights in Science, Engineering and Technology 34 (February 28, 2023): 85–94. http://dx.doi.org/10.54097/hset.v34i.5385.

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Numerous Convolutional Neural Networks emerged in the past decade, each varies in accuracy, speed, and architecture. From AlexNet to ResNet, CNN models have been developing rapidly, and the architecture of the models become more complicated. These models are known for their accuracy on ImageNet, so the topic of this research is to explore how CNN models can perform differently on the Fruit 360 dataset. A model constructed specifically in this research and three significant models developed in the past decade are applied to the Fruit 360 dataset for result comparison: VGG-16, ResNet-50, MobileN

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Zhong, Yansong, Hongyue Lin, Jiacheng Gan, Weiwei You, Jia Chen, and Rongxin Zhang. "Quality Grading of Dried Abalone Using an Optimized VGGNet." Applied Sciences 14, no. 13 (2024): 5894. http://dx.doi.org/10.3390/app14135894.

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As living standards have improved, consumer demand for high-quality dried abalone has increased. Traditional abalone grading is achieved through slice analysis (sampling analysis) combined with human experience. However, this method has several issues, including non-uniform grading standards, low detection accuracy, inconsistency between internal and external quality, and high loss rate. Therefore, we propose a deep-learning-aided approach leveraging X-ray images that can achieve efficient and non-destructive internal quality grading of dried abalone. To the best of our knowledge, this is the

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Manataki, Merope, Nikos Papadopoulos, Nikolaos Schetakis, and Alessio Di Iorio. "Exploring Deep Learning Models on GPR Data: A Comparative Study of AlexNet and VGG on a Dataset from Archaeological Sites." Remote Sensing 15, no. 12 (2023): 3193. http://dx.doi.org/10.3390/rs15123193.

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This comparative study evaluates the performance of three popular deep learning architectures, AlexNet, VGG-16, and VGG-19, on a custom-made dataset of GPR C-scans collected from several archaeological sites. The introduced dataset has 15,000 training images and 3750 test images assigned to three classes: Anomaly, Noise, and Structure. The aim is to assess the performance of the selected architectures applied to the custom dataset and examine the potential gains of using deeper and more complex architectures. Further, this study aims to improve the training dataset using augmentation technique

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Alegavi, Sujata, and Raghvendra Sedamkar. "Optimizing Remote Sensing Image Retrieval Through a Hybrid Methodology." Journal of Imaging 11, no. 6 (2025): 179. https://doi.org/10.3390/jimaging11060179.

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The contemporary challenge in remote sensing lies in the precise retrieval of increasingly abundant and high-resolution remotely sensed images (RS image) stored in expansive data warehouses. The heightened spatial and spectral resolutions, coupled with accelerated image acquisition rates, necessitate advanced tools for effective data management, retrieval, and exploitation. The classification of large-sized images at the pixel level generates substantial data, escalating the workload and search space for similarity measurement. Semantic-based image retrieval remains an open problem due to limi

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Khaleghian, Salman, Habib Ullah, Thomas Kræmer, Nick Hughes, Torbjørn Eltoft, and Andrea Marinoni. "Sea Ice Classification of SAR Imagery Based on Convolution Neural Networks." Remote Sensing 13, no. 9 (2021): 1734. http://dx.doi.org/10.3390/rs13091734.

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We explore new and existing convolutional neural network (CNN) architectures for sea ice classification using Sentinel-1 (S1) synthetic aperture radar (SAR) data by investigating two key challenges: binary sea ice versus open-water classification, and a multi-class sea ice type classification. The analysis of sea ice in SAR images is challenging because of the thermal noise effects and ambiguities in the radar backscatter for certain conditions that include the reflection of complex information from sea ice surfaces. We use manually annotated SAR images containing various sea ice types to cons

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Jamali, Ali, and Masoud Mahdianpari. "Swin Transformer for Complex Coastal Wetland Classification Using the Integration of Sentinel-1 and Sentinel-2 Imagery." Water 14, no. 2 (2022): 178. http://dx.doi.org/10.3390/w14020178.

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The emergence of deep learning techniques has revolutionized the use of machine learning algorithms to classify complicated environments, notably in remote sensing. Convolutional Neural Networks (CNNs) have shown considerable promise in classifying challenging high-dimensional remote sensing data, particularly in the classification of wetlands. State-of-the-art Natural Language Processing (NLP) algorithms, on the other hand, are transformers. Despite the fact that transformers have been utilized for a few remote sensing applications, they have not been compared to other well-known CNN networks

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Zhu, Hengya, Jingjing Qiu, Xiaoyan Sun, Xiangyan Yang, Bin Zhang, and Ying Tan. "Intelligent Algorithm-Based Quantitative Electroencephalography in Evaluating Cerebral Small Vessel Disease Complicated by Cognitive Impairment." Computational and Mathematical Methods in Medicine 2022 (January 29, 2022): 1–9. http://dx.doi.org/10.1155/2022/9398551.

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To analyze the application value of artificial intelligence model based on Visual Geometry Group- (VGG-) 16 combined with quantitative electroencephalography (QEEG) in cerebral small vessel disease (CSVD) with cognitive impairment, 72 patients with CSVD complicated by cognitive impairment were selected as the research subjects. As per Diagnostic and Statistical Manual (5th Edition), they were divided into the vascular dementia (VD) group of 34 cases and vascular cognitive impairment with no dementia (VCIND) group of 38 cases. The two groups were analyzed for the clinical information, neuropsyc

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Alzahrani, Abdullah I. A., Manel Ayadi, Mashael M. Asiri, Amal Al-Rasheed, and Amel Ksibi. "Detecting the Presence of Malware and Identifying the Type of Cyber Attack Using Deep Learning and VGG-16 Techniques." Electronics 11, no. 22 (2022): 3665. http://dx.doi.org/10.3390/electronics11223665.

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Malware is malicious software (harmful program files) that targets and damage computers, devices, networks, and servers. Many types of malware exist, including worms, viruses, trojan horses, etc. With the increase in technology and devices every day, malware is significantly propagating more and more on a daily basis. The rapid growth in the number of devices and computers and the rise in technology is directly proportional to the number of malicious attacks—most of these attacks target organizations, customers, companies, etc. The main goal of these attacks is to steal critical data and passw

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Yang, Wenxi. "A Joint Network Based CNN for Yoga Pose Classification and Scoring." Highlights in Science, Engineering and Technology 23 (December 3, 2022): 161–67. http://dx.doi.org/10.54097/hset.v23i.3218.

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Comparing to traditional rehabilitation, rehabilitation at home becomes a need during pandemic. The technique brought up in this paper allows patients and yoga fans exercise at home with low cost and comfort while can also evaluate their postures. Previous works focus either on classifying poses or scoring on the sameness between the two input branches of patients’ poses and normative poses, but they ignore the combination of them in one single network. In this study, a residual block based Siamese CNN network with classification and scoring modules is proposed, aiming at providing accurate po

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Aji, Wahyu Sapto, Kamarul Hawari Bin Ghazali, and Son Ali Akbar. "Oil palm unstripped bunch detector using modified faster regional convolutional neural network." IAES International Journal of Artificial Intelligence (IJ-AI) 11, no. 1 (2022): 189. http://dx.doi.org/10.11591/ijai.v11.i1.pp189-200.

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The palm oil processing industry in Malaysia and Indonesia is significant and plays a vital role in the community's welfare. The efficiency of palm oil mills is characterized by the low number of unstripped bunch (USBs), so USB detection is essential in the palm oil production process. So far, USB detection is done manually and is often ignored because it is labor-intensive. We developed a USB detector based on faster regional convolutional neural network with a modified visual geometry group 16 (VGG16) backbone to solve this problem. To see the performance of our proposed USB detector, we com

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Wahyu, Sapto Aji, Hawari Bin Ghazali Kamarul, and Ali Akbar Son. "Oil palm unstripped bunch detector using modified faster regional convolutional neural network." International Journal of Artificial Intelligence (IJ-AI) 11, no. 1 (2022): 189–200. https://doi.org/10.11591/ijai.v11.i1.pp189-200.

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The palm oil processing industry in Malaysia and Indonesia is significant and plays a vital role in the community's welfare. The efficiency of palm oil mills is characterized by the low number of unstripped bunch (USBs), so USB detection is essential in the palm oil production process. So far, USB detection is done manually and is often ignored because it is labor-intensive. We developed a USB detector based on faster regional convolutional neural network with a modified visual geometry group 16 (VGG16) backbone to solve this problem. To see the performance of our proposed USB detector, we

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Rashmi, Mothkur, and Budhihal Nagendrappa Veerappa. "An optimal model for classification of lung cancer using grey wolf optimizer and deep hybrid learning." An optimal model for classification of lung cancer using grey wolf optimizer and deep hybrid learning 30, no. 1 (2023): 406–13. https://doi.org/10.11591/ijeecs.v30.i1.pp406-413.

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In recent years, metaheuristic methods have shown major advantages in the field of feature selection due to its comprehensibility and possible extensive search competence. However, the majority of evolutionary computationbased feature selection algorithms in use today are wrapper approaches, which are expensive to compute, particularly for extensive biomedical data. Developing an effective evaluation strategy is crucial for significant reduction of computational cost. The proposed framework extracts deep feature from ResNet-50 and VGG-16 based convolutional neural models with initial segmentat

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Adhikary, Subhrangshu, Saikat Banerjee, Rajani Singh, and Ashutosh Dhar Dwivedi. "Fish species identification on low resolution—a study with enhanced super-resolution generative adversarial network (ESRGAN), YOLO and VGG-16." PeerJ Computer Science 11 (April 30, 2025): e2860. https://doi.org/10.7717/peerj-cs.2860.

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An intelligent detection and recognition model for the fish species from camera footage is urgently required as fishery contributes to a large portion of the world economy, and these kinds of advanced models can aid fishermen on a large scale. Such models incorporating a pick-and-place machine can be beneficial to sorting different fish species in bulk without human intervention, significantly reducing costs for large-scale fishing industries. Existing methods for detecting and recognizing fish species have many limitations, such as limited scalability, detection accuracy, failure to detect mu

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Jamali, Ali, and Masoud Mahdianpari. "Swin Transformer and Deep Convolutional Neural Networks for Coastal Wetland Classification Using Sentinel-1, Sentinel-2, and LiDAR Data." Remote Sensing 14, no. 2 (2022): 359. http://dx.doi.org/10.3390/rs14020359.

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The use of machine learning algorithms to classify complex landscapes has been revolutionized by the introduction of deep learning techniques, particularly in remote sensing. Convolutional neural networks (CNNs) have shown great success in the classification of complex high-dimensional remote sensing imagery, specifically in wetland classification. On the other hand, the state-of-the-art natural language processing (NLP) algorithms are transformers. Although the transformers have been studied for a few remote sensing applications, the integration of deep CNNs and transformers has not been stud

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Malini, A., P. Priyadharshini, and S. Sabeena. "An automatic assessment of road condition from aerial imagery using modified VGG architecture in faster-RCNN framework." Journal of Intelligent & Fuzzy Systems 40, no. 6 (2021): 11411–22. http://dx.doi.org/10.3233/jifs-202596.

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To develop a surveillance and detection system for automating the process of road maintenance work which is being carried out by surveying and inspection of roads manually in the current situation. The need of the system lies in the fact that traditional methods are time-consuming, tiresome and require huge workforce. This paper proposes an automation system using Unmanned Aerial Vehicle which monitors and detects the pavement defects like cracks and potholes by processing real-time video footage of Indian highways. The collected data is processed and stored as images in a road defects databas

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Rahul Kumar. "An Enhanced Oppositional Crow Search Optimization Algorithm-based Colour Edge Segmentation and Modified Resnet-39 Architecture for Prediction of Crop Disease." Journal of Information Systems Engineering and Management 10, no. 5s (2025): 149–61. https://doi.org/10.52783/jisem.v10i5s.599.

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Accurate and timely prediction of crop diseases is vital for improving agricultural productivity and ensuring food security. This research work proposes new framework that integrates an improved swarm-based color edge segmentation method with a modified ResNet-39 architecture to efficiently detect and categorize crop diseases. The suggested segmentation technique utilizes Crow Search optimization algorithm combined with oppositional learning to improve edge recognition accuracy using color segmentation. This allows precise localization of disease-affected areas even under difficult situations

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Reza, Ahmed Wasif, Muhammad Sazzad Hossain, Moonwar Al Wardiful, et al. "A CNN-Based Strategy to Classify MRI-Based Brain Tumors Using Deep Convolutional Network." Applied Sciences 13, no. 1 (2022): 312. http://dx.doi.org/10.3390/app13010312.

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Brain tumor is a severe health condition that kills many lives every year, and several of those casualties are from rural areas. However, the technology to diagnose brain tumors at an early stage is not as efficient as expected. Therefore, we sought to create a reliable system that can help medical professionals to identify brain tumors. Although several studies are being conducted on this issue, we attempted to establish a much more efficient and error-free classification method, which is trained with a comparatively substantial number of real datasets rather than augmented data. Using a modi

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Ubaid, Muhammad Talha, and Sameena Javaid. "Precision Agriculture: Computer Vision-Enabled Sugarcane Plant Counting in the Tillering Phase." Journal of Imaging 10, no. 5 (2024): 102. http://dx.doi.org/10.3390/jimaging10050102.

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The world’s most significant yield by production quantity is sugarcane. It is the primary source for sugar, ethanol, chipboards, paper, barrages, and confectionery. Many people are affiliated with sugarcane production and their products around the globe. The sugarcane industries make an agreement with farmers before the tillering phase of plants. Industries are keen on knowing the sugarcane field’s pre-harvest estimation for planning their production and purchases. The proposed research contribution is twofold: by publishing our newly developed dataset, we also present a methodology to estimat

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Senthilkumar, C., Eatedal Alabdulkreem, Nuha Alruwais, and K. Suresh. "Multimodal Brain Tumor Classification using Capsule Convolution Neural Network with Differential Evolution Optimization Process." Measurement Science Review 24, no. 6 (2024): 234–38. https://doi.org/10.2478/msr-2024-0031.

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Abstract Manual identification of brain tumors is error-prone and time-consuming for radiologists. Therefore, automation of the process is crucial. Although binary classification, such as distinguishing between malignant and benign tumors, is often straightforward, radiologists face significant challenges when classifying multimodal brain tumors. In this study, we present an automated approach that uses deep learning to classify brain tumor types using many types of data. The proposed method consists of three sequential phases. First, the median filter is used to eliminate any noise. For featu

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Satybaldina, Dina, and Gulzia Kalymova. "Deep learning based static hand gesture recognition." Indonesian Journal of Electrical Engineering and Computer Science 21, no. 1 (2021): 398. http://dx.doi.org/10.11591/ijeecs.v21.i1.pp398-405.

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Hand gesture recognition becomes a popular topic of deep learning and provides many application fields for bridging the human–computer barrier and has a positive impact on our daily life. The primary idea of our project is a static gesture acquisition from depth camera and to process the input images to train the deep convolutional neural network pre-trained on ImageNet dataset. Proposed system consists of gesture capture device (Intel® RealSense™ depth camera D435), pre-processing and image segmentation algorithms, feature extraction algorithm and object classification. For pre-processing and

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Mothkur, Rashmi, and Veerappa Budhihal Nagendrappa. "An optimal model for classification of lung cancer using grey wolf optimizer and deep hybrid learning." Indonesian Journal of Electrical Engineering and Computer Science 30, no. 1 (2023): 406. http://dx.doi.org/10.11591/ijeecs.v30.i1.pp406-413.

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In recent years, metaheuristic methods have shown major advantages in the field of feature selection due to its comprehensibility and possible extensive search competence. However, the majority of evolutionary computation-based feature selection algorithms in use today are wrapper approaches, which are expensive to compute, particularly for extensive biomedical data. Developing an effective evaluation strategy is crucial for significant reduction of computational cost. The proposed framework extracts deep feature from ResNet-50 and VGG-16 based convolutional neural models with initial segmenta

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Rajesh Yakkundimath, Ramesh Badiger, Naveen Malvade. "“Deep Learning Based Classification of Single-Hand South Indian Sign Language Gestures”." Journal of Electrical Systems 20, no. 2s (2024): 200–209. http://dx.doi.org/10.52783/jes.1128.

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Gesture recognition is a branch of computer science and language technology dedicated to utilizing mathematical algorithms for the analysis of human gestures. Within the realm of non-verbal communication, the pivotal role of human arm movements and gestures remains a focal point. This research introduces advanced multi-stream deep transfer learning models tailored for identifying signs from South Indian languages, specifically Kannada, Tamil, and Telugu. The primary aim is to offer support to individuals encountering speech disorders or disabilities. The key deep transfer learning models utili

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Naveen Malvade, Rajesh Yakkundimath ,Ramesh Badiger ,. "“Deep Learning Based Classification of Single-Hand South Indian Sign Language Gestures”." Journal of Electrical Systems 20, no. 1s (2024): 702–11. http://dx.doi.org/10.52783/jes.813.

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Gesture recognition is a branch of computer science and language technology dedicated to utilizing mathematical algorithms for the analysis of human gestures. Within the realm of non-verbal communication, the pivotal role of human arm movements and gestures remains a focal point. This research introduces advanced multi-stream deep transfer learning models tailored for identifying signs from South Indian languages, specifically Kannada, Tamil, and Telugu. The primary aim is to offer support to individuals encountering speech disorders or disabilities. The key deep transfer learning models utili

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Ganesan, Praveena, G. P. Ramesh, C. Puttamdappa, and Yarlagadda Anuradha. "A Modified Bio-Inspired Optimizer with Capsule Network for Diagnosis of Alzheimer Disease." Applied Sciences 14, no. 15 (2024): 6798. http://dx.doi.org/10.3390/app14156798.

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Recently, Alzheimer’s disease (AD) is one of the common neurodegenerative disorders, which primarily occurs in old age. Structural magnetic resonance imaging (sMRI) is an effective imaging technique used in clinical practice for determining the period of AD patients. An efficient deep learning framework is proposed in this paper for AD detection, which is inspired from clinical practice. The proposed deep learning framework significantly enhances the performance of AD classification by requiring less processing time. Initially, in the proposed framework, the sMRI images are acquired from a rea

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Morani, Kenan, Esra Kaya Ayana, and Devrim Unay. "Covid-19 detection using modified xception transfer learning approach from computed tomography images." International Journal of Advances in Intelligent Informatics 9, no. 3 (2023): 524. http://dx.doi.org/10.26555/ijain.v9i3.1432.

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The significance of efficient and accurate diagnosis amidst the unique challenges posed by the COVID-19 pandemic underscores the urgency for innovative approaches. In response to these challenges, we propose a transfer learning-based approach using a recently annotated Computed Tomography (CT) image database. While many approaches propose an intensive data preprocessing and/or complex model architecture, our method focuses on offering an efficient solution with minimal manual engineering. Specifically, we investigate the suitability of a modified Xception model for COVID-19 detection. The meth

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Hassan, Esraa, Samir Elmougy, Mai R. Ibraheem, et al. "Enhanced Deep Learning Model for Classification of Retinal Optical Coherence Tomography Images." Sensors 23, no. 12 (2023): 5393. http://dx.doi.org/10.3390/s23125393.

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Retinal optical coherence tomography (OCT) imaging is a valuable tool for assessing the condition of the back part of the eye. The condition has a great effect on the specificity of diagnosis, the monitoring of many physiological and pathological procedures, and the response and evaluation of therapeutic effectiveness in various fields of clinical practices, including primary eye diseases and systemic diseases such as diabetes. Therefore, precise diagnosis, classification, and automated image analysis models are crucial. In this paper, we propose an enhanced optical coherence tomography (EOCT)

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Benoy Abraham, R. S. Vinod Kumar, S. S. Kumar. "A Deep Learning Network for Classification of Lung Cancer from Computer Tomography Images Using Fine-Tuned Visual Geometric Group-16." Journal of Information Systems Engineering and Management 10, no. 2 (2025): 579–94. https://doi.org/10.52783/jisem.v10i2.2452.

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Many deaths from cancer are caused by lung cancer, of the most prevalent and deadly forms of the illness. Although lung cancer remains a significant health issue, improvements in research, early detection techniques, and current treatments give hope for improved outcomes. To diagnosis a wide range of diseases, numerous Computer Aided Diagnosis (CAD) systems have been created recently. Early lung cancer detection is now crucial and simple thanks to deep learning and image processing methods. For radiologists, identifying cancerous lung nodules is a difficult and time-consuming process that invo

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Satybaldina, Dina, and Gulzia Kalymova. "Deep learning based static hand gesture recognition." Indonesian Journal of Electrical Engineering and Computer Science 21, no. 1 (2021): 398–405. https://doi.org/10.11591/ijeecs.v21.i1.pp398-405.

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Hand gesture recognition becomes a popular topic of deep learning and provides many application fields for bridging the human-computer barrier and has a positive impact on our daily life. The primary idea of our project is a static gesture acquisition from depth camera and to process the input images to train the deep convolutional neural network pre-trained on ImageNet dataset. Proposed system consists of gesture capture device (Intel® RealSense™ depth camera D435), pre-processing and image segmentation algorithms, feature extraction algorithm and object classification. For preproce

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S, Renuga, Malathi P, Shamija Sherryl R.M.R, Anuradha T, Mishmala Sushith, and Senthil Kumar A. "Deep Learning Techniques for MRI Image-Based Performance Analysis of Brain Tumor Classification." Journal of Applied Engineering and Technological Science (JAETS) 6, no. 1 (2024): 593–609. https://doi.org/10.37385/jaets.v6i1.6288.

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Brain tumors can produce symptoms and indicators due to direct tissue death, localized invasion of the brain, or aftereffects from increased intracranial pressure. In order to identify images from the publicly available image dataset, this work combined multiple image feature sources using deep learning algorithms. The architecture of most classic convolutional neural networks (CNNs) consists of convolution modification and max-pooling of layers connected with several completely linked layers. The steps used in this system are pre-processing, segmentation, feature extraction, and classificatio

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Alnowaiser, Khaled, Abeer Saber, Esraa Hassan, and Wael A. Awad. "An optimized model based on adaptive convolutional neural network and grey wolf algorithm for breast cancer diagnosis." PLOS ONE 19, no. 8 (2024): e0304868. http://dx.doi.org/10.1371/journal.pone.0304868.

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Medical image classification (IC) is a method for categorizing images according to the appropriate pathological stage. It is a crucial stage in computer-aided diagnosis (CAD) systems, which were created to help radiologists with reading and analyzing medical images as well as with the early detection of tumors and other disorders. The use of convolutional neural network (CNN) models in the medical industry has recently increased, and they achieve great results at IC, particularly in terms of high performance and robustness. The proposed method uses pre-trained models such as Dense Convolutiona

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Gadekar,, Supriya S. "Roadmap for Digital Image Forgery Detection Using Deep Learning." Tuijin Jishu/Journal of Propulsion Technology 44, no. 5 (2023): 1732–48. http://dx.doi.org/10.52783/tjjpt.v44.i5.2849.

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Technology advances are prominent today while influencing all aspects of our lives. Misuse of information has also increased as a result of technical improvements. As a result, investigators have the enormous task of recognizing modified information and distinguishing this from genuine data. Among the most prevalent techniques for electronic image alteration is splicing, which includes replicating a specific section using the same or different photograph and transferring it to a new image. In the wake of this issue, picture identification of forgeries has arisen as a viable approach for confir

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Yang, Ming-Der, Hsin-Hung Tseng, Yu-Chun Hsu, Chin-Ying Yang, Ming-Hsin Lai, and Dong-Hong Wu. "A UAV Open Dataset of Rice Paddies for Deep Learning Practice." Remote Sensing 13, no. 7 (2021): 1358. http://dx.doi.org/10.3390/rs13071358.

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Recently, unmanned aerial vehicles (UAVs) have been broadly applied to the remote sensing field. For a great number of UAV images, deep learning has been reinvigorated and performed many results in agricultural applications. The popular image datasets for deep learning model training are generated for general purpose use, in which the objects, views, and applications are for ordinary scenarios. However, UAV images possess different patterns of images mostly from a look-down perspective. This paper provides a verified annotated dataset of UAV images that are described in data acquisition, data

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Maqsood, Sarmad, Robertas Damaševičius, and Rytis Maskeliūnas. "TTCNN: A Breast Cancer Detection and Classification towards Computer-Aided Diagnosis Using Digital Mammography in Early Stages." Applied Sciences 12, no. 7 (2022): 3273. http://dx.doi.org/10.3390/app12073273.

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Breast cancer is a major research area in the medical image analysis field; it is a dangerous disease and a major cause of death among women. Early and accurate diagnosis of breast cancer based on digital mammograms can enhance disease detection accuracy. Medical imagery must be detected, segmented, and classified for computer-aided diagnosis (CAD) systems to help the radiologists for accurate diagnosis of breast lesions. Therefore, an accurate breast cancer detection and classification approach is proposed for screening of mammograms. In this paper, we present a deep learning system that can

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et al., Muhammad. "Deep transfer learning CNN based approach for COVID-19 detection." International Journal of ADVANCED AND APPLIED SCIENCES 9, no. 4 (2022): 44–52. http://dx.doi.org/10.21833/ijaas.2022.04.006.

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Recently, the novel coronavirus (Covid-19) and its different variants have spread rapidly across the world. Early-stage detection of COVID-19 is a challenging task due to the limited availability of Covid testing kits to the public. Conventionally, reverse transcription-polymerase chain reaction (RT-PCR) is the reliable test for the detection of COVID-19 which is time-consuming and costly. The aim of this work is to identify the COVID-19 symptoms with the help of a deep learning algorithm using chest X-Ray images. In order to improve the quality of chest X-Ray images, authors have further modi

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