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Journal articles on the topic 'InceptionResNetV2'

Author: Grafiati

Published: 11 September 2023

Last updated: 31 July 2025

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1

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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Wijaya, Jodi, and Kusrini Kusrini. "Classification of Coffee Fruit Ripe Levels from the Tree Using InceptionResnetv2, and MobileNetv2." Bitnet: Jurnal Pendidikan Teknologi Informasi 10, no. 1 (2025): 70–79. https://doi.org/10.33084/bitnet.v10i1.9278.

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Tanaman kopi adalah komoditas ekpor terbesar dalam peningkatan ekonomi negara berkembang. Kopi memiliki peran penting dalam budaya Indonesia, dengan sejarah panjang dan berbagai citra rasa yang kaya. Ada dua jenis utama kopi di Indonesia: Arabika dan Robusta. Kematangan dan pengolahan biji kopi menjadi salah satu pengaruh citra rasa kopi. Untuk menghasilkan biji kopi berkualitas tinggi, proses pemanenan biji kopi yang tepat pada tingkat kematangan yang ideal sangat penting. MobileNet adalah arsitektur jaringan saraf tiruan CNN yang menggunakan konvolusi yang dipisahkan secara kedalaman (depthw

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Kurniawan, Vallent Austin Theasar, Elan Cahya Niswary, Christian S.k.aditya, and Didih Rizki Chandranegara. "BRAIN TUMOR CLASSIFICATION USING INCEPTIONRESNET-V2 AND TRANSFER LEARNING APPROACH." JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) 10, no. 1 (2024): 91–99. http://dx.doi.org/10.33480/jitk.v10i1.5223.

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Brain, a highly intricate organ within the central nervous system, plays a fundamental role in information processing, cognition, motor control, and consciousness. Brain tumors pose severe threats to brain function and overall human well-being. Timely detection of these tumors is imperative for life-saving interventions. A dataset comprising four categories: no tumors, meningioma tumors, glioma tumors, and pituitary tumors was regarded in this research. The employed of the InceptionResNet-V2 architecture combined with Transfer Learning and data augmentation proposed to obtain optimal results o

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Yazid Aufar, Muhammad Helmy Abdillah, and Jiki Romadoni. "Web-based CNN Application for Arabica Coffee Leaf Disease Prediction in Smart Agriculture." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 7, no. 1 (2023): 71–79. http://dx.doi.org/10.29207/resti.v7i1.4622.

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In the agriculture industry, plant diseases provide difficulty, particularly for Arabica coffee production. A first step in eliminating and treating infections to avoid crop damage is recognizing ailments on Arabica coffee leaves. Convolutional neural networks (CNN) are rapidly advancing, making it possible to diagnose Arabica coffee leaf damage without a specialist's help. CNN is aimed to find features adaptively through backpropagation by adding layers including convolutional layers and pooling layers. This study aims to optimize and increase the accuracy of Arabica coffee leaf disease class

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PROCHUKHAN, Dmitro. "FEATURES OF THE MODIFICATION OF THE INCEPTIONRESNETV2 ARCHITECTURE AND THE CREATION OF A DIAGNOSTIC SYSTEM FOR DETERMINING THE DEGREE OF DAMAGE TO RETINAL VESSELS." Computer systems and information technologies, no. 1 (March 28, 2024): 27–32. http://dx.doi.org/10.31891/csit-2024-1-3.

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Diabetic retinopathy is a retinal disease caused by diabetes. The progression of this disease can lead to blindness. Every year, the number of patients with this disease increases. Diabetic retinal damage can be slowed if it is diagnosed early. The article describes the features of the creation of a neural network model and the development of a system with high accuracy rates for the recognition of diabetic retinopathy. The advantages of the InceptionResNetv2 convolutional neural network architecture are considered. This network uses residual connections that help facilitate the learning proce

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Ninama, Hitesh, Jagdish Raikwal, and Pushpa Raikwal. "Ensemble Transfer Learning for Automated Gauge Reading Detection and Prediction." Scalable Computing: Practice and Experience 26, no. 5 (2025): 2130–44. https://doi.org/10.12694/scpe.v26i5.4786.

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Pressure gauges and automatic reading methods for pointer gauges were the root causes of the problems that have motivated the development of an ensemble transfer learning strategy. The study suggests that to effectively generate and predict the present measurements of the gauge, it is necessary to use an ensemble learning method that incorporates transfer learning framework designs such as InceptionResnetV2 or DenseNet 201. The suggested methodology involves integrating the given data with ensemble model architectures and qualifying InceptionResnetV2 and Dense Net 201 models to forecast the pr

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Jiang, Kaiyuan, Jiawei Zhang, Haibin Wu, Aili Wang, and Yuji Iwahori. "A Novel Digital Modulation Recognition Algorithm Based on Deep Convolutional Neural Network." Applied Sciences 10, no. 3 (2020): 1166. http://dx.doi.org/10.3390/app10031166.

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The modulation recognition of digital signals under non-cooperative conditions is one of the important research contents here. With the rapid development of artificial intelligence technology, deep learning theory is also increasingly being applied to the field of modulation recognition. In this paper, a novel digital signal modulation recognition algorithm is proposed, which has combined the InceptionResNetV2 network with transfer adaptation, called InceptionResnetV2-TA. Firstly, the received signal is preprocessed and generated the constellation diagram. Then, the constellation diagram is us

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Kalkan, Muruvvet, Mehmet S. Guzel, Fatih Ekinci, Ebru Akcapinar Sezer, and Tunc Asuroglu. "Comparative Analysis of Deep Learning Methods on CT Images for Lung Cancer Specification." Cancers 16, no. 19 (2024): 3321. http://dx.doi.org/10.3390/cancers16193321.

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Background: Lung cancer is the leading cause of cancer-related deaths worldwide, ranking first in men and second in women. Due to its aggressive nature, early detection and accurate localization of tumors are crucial for improving patient outcomes. This study aims to apply advanced deep learning techniques to identify lung cancer in its early stages using CT scan images. Methods: Pre-trained convolutional neural networks (CNNs), including MobileNetV2, ResNet152V2, InceptionResNetV2, Xception, VGG-19, and InceptionV3, were used for lung cancer detection. Once the disease was identified, the tum

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Huang, Youran. "Research on garbage sorting robotic arm based on image vision." Journal of Physics: Conference Series 2741, no. 1 (2024): 012020. http://dx.doi.org/10.1088/1742-6596/2741/1/012020.

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Abstract This paper aims to investigate the application of artificial intelligence in robotic arm automation control for the accurate identification of different types of garbage using deep learning algorithms. The goal is to enable the robotic arm to autonomously classify and handle garbage. The appropriate garbage classification dataset was selected and subjected to data preprocessing in this study. After comparing various well-established convolutional neural network models, including VGG16, InceptionResNetV2, Xception, and InceptionResNetV3, in terms of performance and suitability on the t

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Cheritha, K., S. Akhil, V. Bhanu Prakash, and A. Akhil Reddy. "Deepfake Detection using Deep Learning with InceptionV3." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem44058.

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Deepfake technology has rapidly evolved, making it increasingly difficult to distinguish between real and manipulated videos. This poses serious risks, including misinformation, identity theft, and digital forgery. To address this challenge, we propose a deep learning-based deepfake detection model that leverages InceptionResNetV2, a hybrid architecture combining the strengths of Inception networks and Residual networks (ResNet). Our approach efficiently extracts key facial features from video frames and classifies them as real or fake. The detection pipeline includes video preprocessing, fram

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Liana, Verianti, Rizal Arifiandika, Bagas Rohmatulloh, et al. "Enzyme dosage detection to degrade feathers in edible bird’s nests: A comparative convolutional neural networks study." Advances in Food Science, Sustainable Agriculture and Agroindustrial Engineering 6, no. 4 (2023): 382–98. http://dx.doi.org/10.21776/ub.afssaae.2023.006.04.6.

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Edible Bird’s Nest (EBN), a costly food product made from swiftlet’s saliva, has encountered a longstanding problem of plucking the swiftlet’s feather from the nests. The destructive and inefficient manual process of plucking the feathers can be substituted with a serine protease enzyme alternative. Accurate detection of enzyme dosage is crucial for ensuring efficient feather degradation with cost-effective enzyme usage. This research employed the transfer learning method using pretrained Convolutional Neural Networks (Pt-CNN) to detect enzyme dosage based on EBN’s images. This study aimed to

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Shim, Jae-Hyuk, Woongsang Sunwoo, Byung Yoon Choi, Kwang Gi Kim, and Young Jae Kim. "Improving the Accuracy of Otitis Media with Effusion Diagnosis in Pediatric Patients Using Deep Learning." Bioengineering 10, no. 11 (2023): 1337. http://dx.doi.org/10.3390/bioengineering10111337.

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Otitis media with effusion (OME), primarily seen in children aged 2 years and younger, is characterized by the presence of fluid in the middle ear, often resulting in hearing loss and aural fullness. While deep learning networks have been explored to aid OME diagnosis, prior work did not often specify if pediatric images were used for training, causing uncertainties about their clinical relevance, especially due to important distinctions between the tympanic membranes of small children and adults. We trained cross-validated ResNet50, DenseNet201, InceptionV3, and InceptionResNetV2 models on 11

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Bhargav, Shivayogi B. Ullagaddi. "Pre-Trained Deep Learning Models for Detecting Strikeouts in Kannada Handwritten Documents." Tuijin Jishu/Journal of Propulsion Technology 44, no. 3 (2023): 1649–58. http://dx.doi.org/10.52783/tjjpt.v44.i3.556.

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In the digital document analysis, handwritten character recognition is a challenging area. Various methods are proposed in the literature to identify the strike-outs in various languages. Kannada handwritten classification is a crucial machine vision problem due to its various practical applications in the development of recognition systems. Identifying of strikeout string on the given Kannada statement is an important challenge to develop robust recognition system. In the present study, pre-trained models such as DenseNet121, EficientNetB0, MobileNet, InceptionResNetV2 are used to recognize s

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Wu, Ling, Yongyi Jinma, Xinyang Wang, et al. "Amur Tiger Individual Identification Based on the Improved InceptionResNetV2." Animals 14, no. 16 (2024): 2312. http://dx.doi.org/10.3390/ani14162312.

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Accurate and intelligent identification of rare and endangered individuals of flagship wildlife species, such as Amur tiger (Panthera tigris altaica), is crucial for understanding population structure and distribution, thereby facilitating targeted conservation measures. However, many mathematical modeling methods, including deep learning models, often yield unsatisfactory results. This paper proposes an individual recognition method for Amur tigers based on an improved InceptionResNetV2 model. Initially, the YOLOv5 model is employed to automatically detect and segment facial, left stripe, and

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Uskaner Hepsağ, Pınar. "Modifiye-InceptionResNetV2 Mimarisi Kullanarak Domates Yaprak Koşullarının Etkili Tespiti." Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi 8, no. 1 (2025): 375–92. https://doi.org/10.47495/okufbed.1443018.

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Domates yapraklarını etkileyen hastalıkların zamanında tespit edilmesi ve tedavi edilmesi, bitki üretkenliğini, operasyonel verimliliği ve genel kaliteyi artırmak için esastır. Domates bitkileri çeşitli hastalıklara oldukça duyarlıdır ve çiftçilerin bu hastalıkları yanlış teşhis etmeleri, yetersiz tedavi stratejilerine yol açarak hem bitkilere hem de tarım ekosistemine zarar verebilir. Domates mahsullerinin kalitesinin sağlanması, zamanında ve doğru teşhise büyük ölçüde bağlıdır. Günümüzde derin öğrenme teknikleri, domates bitkilerinde hastalıkları sınıflandırmak gibi çeşitli uygulamalarda öne

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Kavita, Thakur, Kaur Sandhu Navneet, and Kumar Yogesh. "Automated System for Prediction and Prognosis of Infection Diseases Using Deep Learning-Based Approaches." Indian Journal of Science and Technology 16, no. 34 (2023): 2730–39. https://doi.org/10.17485/IJST/v16i34.1306.

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Abstract <strong>Objectives:</strong> This study explores the potential of deep learning-based techniques to improve disease management and intervention by focusing on their use in infectious disease prediction and prognosis. <strong>Methods:</strong> The research used deep learning models EfficientNetB0, NASNetLarge, DenseNet169, ResNet152V2, and InceptionResNetV2. For this study, a dataset comprising 29,252 images of different diseases such as COVID-19, MERS, Pneumonia, SARS, and tuberculosis. To visualize pixel intensity, exploratory data analysis was performed on the picture

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Firnando, Fadel Muhamad, De Rosal Ignatius Moses Setiadi, Ahmad Rofiqul Muslikh, and Syahroni Wahyu Iriananda. "Analyzing InceptionV3 and InceptionResNetV2 with Data Augmentation for Rice Leaf Disease Classification." Journal of Future Artificial Intelligence and Technologies 1, no. 1 (2024): 1–11. http://dx.doi.org/10.62411/faith.2024-4.

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This research aims to evaluate and compare the performance of several deep learning architectures, especially InceptionV3 and InceptionResNetV2, with other models, such as EfficientNetB3, ResNet50, and VGG19, in classifying rice leaf diseases. In addition, this research also evaluates the impact of using data augmentation on model performance. Three different datasets were used in this experiment, varying the number of images and class distribution. The results show that InceptionV3 and InceptionResNetV2 consistently perform excellently and accurately on most datasets. Data augmentation has va

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Faruk, Omar, Eshan Ahmed, Sakil Ahmed, et al. "A Novel and Robust Approach to Detect Tuberculosis Using Transfer Learning." Journal of Healthcare Engineering 2021 (November 25, 2021): 1–10. http://dx.doi.org/10.1155/2021/1002799.

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Deep learning has emerged as a promising technique for a variety of elements of infectious disease monitoring and detection, including tuberculosis. We built a deep convolutional neural network (CNN) model to assess the generalizability of the deep learning model using a publicly accessible tuberculosis dataset. This study was able to reliably detect tuberculosis (TB) from chest X-ray images by utilizing image preprocessing, data augmentation, and deep learning classification techniques. Four distinct deep CNNs (Xception, InceptionV3, InceptionResNetV2, and MobileNetV2) were trained, validated

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Al-Timemy, Ali H., Laith Alzubaidi, Zahraa M. Mosa, et al. "A Deep Feature Fusion of Improved Suspected Keratoconus Detection with Deep Learning." Diagnostics 13, no. 10 (2023): 1689. http://dx.doi.org/10.3390/diagnostics13101689.

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Detection of early clinical keratoconus (KCN) is a challenging task, even for expert clinicians. In this study, we propose a deep learning (DL) model to address this challenge. We first used Xception and InceptionResNetV2 DL architectures to extract features from three different corneal maps collected from 1371 eyes examined in an eye clinic in Egypt. We then fused features using Xception and InceptionResNetV2 to detect subclinical forms of KCN more accurately and robustly. We obtained an area under the receiver operating characteristic curves (AUC) of 0.99 and an accuracy range of 97–100% to

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Cheng, Wen-Chang, Hung-Chou Hsiao, Yung-Fa Huang, and Li-Hua Li. "Combining Classifiers for Deep Learning Mask Face Recognition." Information 14, no. 7 (2023): 421. http://dx.doi.org/10.3390/info14070421.

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This research proposes a single network model architecture for mask face recognition using the FaceNet training method. Three pre-trained convolutional neural networks of different sizes are combined, namely InceptionResNetV2, InceptionV3, and MobileNetV2. The models are augmented by connecting an otherwise fully connected network with a SoftMax output layer. We combine triplet loss and categorical cross-entropy loss to optimize the training process. In addition, the learning rate of the optimizer is dynamically updated using the cosine annealing mechanism, which improves the convergence of th

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Mahdianpari, Masoud, Bahram Salehi, Mohammad Rezaee, Fariba Mohammadimanesh, and Yun Zhang. "Very Deep Convolutional Neural Networks for Complex Land Cover Mapping Using Multispectral Remote Sensing Imagery." Remote Sensing 10, no. 7 (2018): 1119. http://dx.doi.org/10.3390/rs10071119.

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Despite recent advances of deep Convolutional Neural Networks (CNNs) in various computer vision tasks, their potential for classification of multispectral remote sensing images has not been thoroughly explored. In particular, the applications of deep CNNs using optical remote sensing data have focused on the classification of very high-resolution aerial and satellite data, owing to the similarity of these data to the large datasets in computer vision. Accordingly, this study presents a detailed investigation of state-of-the-art deep learning tools for classification of complex wetland classes

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Fatima, Unse, Zafran Khan, Jeonghwan Gwak, and Moongu Jeon. "IRAE-UNet: InceptionResNetV2 -Attention Encoder based UNet Semantic Segmentation of Aerial Imagery." KIISE Transactions on Computing Practices 30, no. 9 (2024): 484–89. http://dx.doi.org/10.5626/ktcp.2024.30.9.484.

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Pohtongkam, Somchai, and Jakkree Srinonchat. "Tactile Object Recognition for Humanoid Robots Using New Designed Piezoresistive Tactile Sensor and DCNN." Sensors 21, no. 18 (2021): 6024. http://dx.doi.org/10.3390/s21186024.

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A tactile sensor array is a crucial component for applying physical sensors to a humanoid robot. This work focused on developing a palm-size tactile sensor array (56.0 mm × 56.0 mm) to apply object recognition for the humanoid robot hand. This sensor was based on a PCB technology operating with the piezoresistive principle. A conductive polymer composites sheet was used as a sensing element and the matrix array of this sensor was 16 × 16 pixels. The sensitivity of this sensor was evaluated and the sensor was installed on the robot hand. The tactile images, with resolution enhancement using bic

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MATHARASI,, P. BAVITHRA. "Harvest Guard: Crop Loss Detection Enhanced with Inception-Based Models." International Scientific Journal of Engineering and Management 04, no. 04 (2025): 1–7. https://doi.org/10.55041/isjem02651.

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Diseases of plants are a major risk factor for world agriculture, causing severe crop loss and decreased food yields. This paper discusses the usage of deep learning models, that is InceptionV3 and InceptionResNetV2, in plant disease classification using the PlantVillage dataset. Preprocessing methods like the removal of duplicates and blur from images are performed to improve the performance of the model, and then data augmentation is used for enhanced learning. The data is systematically split into training, validation, and test sets to facilitate proper evaluation. Performance of the model

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Shanmugam, Hemachandiran, and Aghila Gnanasekaran. "Finetuned Deep Learning Models for Fuel Classification: A Transfer Learning-Based Approach." Energies 18, no. 5 (2025): 1176. https://doi.org/10.3390/en18051176.

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Accurately classifying petrol and diesel fuel using an image processing method is crucial for fuel-related industries such as petrol pumps, refineries, and fuel storage facilities. However, distinguishing between these fuels using traditional methods can be challenging due to their similar visual characteristics. This study aims to enhance the accuracy and robustness of existing fuel classification by utilizing the transfer learning-based finetuned pre-trained deep learning models and ensemble approaches. Specifically, we upgrade pre-trained deep models like ResNet152V2, InceptionResNetV2, and

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Firasari, Elly, and F. Lia Dwi Cahyanti. "Implementation of Deep Learning for Potato Leaf Disease Detection Using the InceptionResNetV2 Architecture." Indonesian Journal Computer Science 4, no. 1 (2025): 76–81. https://doi.org/10.31294/n09m6677.

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Kentang (Solanum tuberosum) merupakan salah satu komoditas pangan penting di Indonesia. Namun, produktivitasnya kerap terganggu oleh penyakit daun seperti Early Blight, Late Blight, dan infeksi virus. Identifikasi penyakit secara manual oleh petani masih memiliki keterbatasan dari segi waktu, tenaga, dan akurasi. Penelitian ini bertujuan untuk mengembangkan sistem klasifikasi otomatis penyakit daun kentang menggunakan metode deep learning, khususnya dengan arsitektur InceptionResNetV2. Dataset yang digunakan bersumber dari Kaggle dan terdiri dari 3.251 citra yang terbagi dalam tiga kelas: Heal

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Loorutu, Nalson Mark, Haniza Yazid, and Khairul Shakir Ab Rahman. "Prostate Cancer Classification Based on Histopathological Images." International Journal on Robotics, Automation and Sciences 5, no. 2 (2023): 43–53. http://dx.doi.org/10.33093/ijoras.2023.5.2.5.

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Prostate cancer is a significant health concern, ranking as the third most common cancer in Malaysian men, with increasing incidence in Asia. The importance of automating the prostate cancer classification process lies in its potential to significantly improve diagnostic accuracy, reduce subjectivity, and enhance overall efficiency compared to the manual approach. The objective of this thesis is two-fold: firstly, to effectively enhance and segment crucial features in the images to aid in the classification process, and secondly, to implement a binary classification task that indicates the pre

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Angurala, Mohit. "Augmented MRI Images for Classification of Normal and Tumors Brain through Transfer Learning Techniques." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 5s (2023): 536–42. http://dx.doi.org/10.17762/ijritcc.v11i5s.7130.

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A brain tumor is a severe malignant condition caused by uncontrolled and abnormal cell division. Recent advances in deep learning have aided the health business in Medical Imaging for the diagnosis of numerous disorders. The most frequent and widely used deep learning algorithm for visual learning and image recognition. This research seeks to multi-classification tumors in the brain from images attained by Magnetic Resonance Imaging (MRI) using deep learning models that have been pre-trained for transfer learning. As per the publicly available MRI brain tumor dataset, brain tumors identified a

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Indrawan, I. Gusti Agung, Putu Andy Novit Pranartha, I. Wayan Agus Surya Darma, Ni Putu Sutramiani, and I. Putu Eka Giri Gunawan. "Optimization Strategy on Deep Learning Model to Improve Fruit Freshness Recognition." Lontar Komputer : Jurnal Ilmiah Teknologi Informasi 14, no. 1 (2023): 1. http://dx.doi.org/10.24843/lkjiti.2023.v14.i01.p01.

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The high fruit production during the harvest season is a challenge in the process of sorting fresh fruit and rotten fruit in plantations. Automatic fruit freshness classification based on deep learning can speed up the sorting process. However, building a model with high accuracy requires the right strategy based on the dataset's characteristics. This research aims to apply optimization strategies to deep learning models to improve model performance. The optimization strategy is implemented by optimizing the model using fine-tuning strategy by selecting the best parameters based on learning ra

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Mondal, M. Rubaiyat Hossain, Subrato Bharati, and Prajoy Podder. "CO-IRv2: Optimized InceptionResNetV2 for COVID-19 detection from chest CT images." PLOS ONE 16, no. 10 (2021): e0259179. http://dx.doi.org/10.1371/journal.pone.0259179.

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This paper focuses on the application of deep learning (DL) in the diagnosis of coronavirus disease (COVID-19). The novelty of this work is in the introduction of optimized InceptionResNetV2 for COVID-19 (CO-IRv2) method. A part of the CO-IRv2 scheme is derived from the concepts of InceptionNet and ResNet with hyperparameter tuning, while the remaining part is a new architecture consisting of a global average pooling layer, batch normalization, dense layers, and dropout layers. The proposed CO-IRv2 is applied to a new dataset of 2481 computed tomography (CT) images formed by collecting two ind

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Budi Dwi Satoto, Bain Khusnul Khotimah, Mohammad Yusuf, Rinci Kembang Hapsari, and Budi Irmawati. "Classification of coastal shrimp species using deep learning InceptionResNetV2 with data augmentation." Technium: Romanian Journal of Applied Sciences and Technology 16 (October 29, 2023): 250–58. http://dx.doi.org/10.47577/technium.v16i.9989.

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Coastal areas that are rich in shrimp natural resources usually have a strong fishing industry. Shrimp consumption can contribute to the local economy. Several types of shrimp have higher utilization potential in the fishing industry and human consumption. Classification helps sort out shrimp species in coastal waters. Deep learning technology helps identify and separate shrimp species through deep learning with the Convolutional neural network. The seven classes observed were Vaname Shrimp, Tiger Shrimp, Jerbung Shrimp, Giant Prawns, Red Shrimp, Ronggeng Shrimp, and Prawns. The data used is 5

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Shoaib, Mohamed R., Mohamed R. Elshamy, Taha E. Taha, Adel S. El-Fishawy, and Fathi E. Abd El-Samie. "Efficient Brain Tumor Detection Based on Deep Learning Models." Journal of Physics: Conference Series 2128, no. 1 (2021): 012012. http://dx.doi.org/10.1088/1742-6596/2128/1/012012.

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Abstract Brain tumor is an acute cancerous disease that results from abnormal and uncontrollable cell division. Brain tumors are classified via biopsy, which is not normally done before the brain ultimate surgery. Recent advances and improvements in deep learning technology helped the health industry in getting accurate disease diagnosis. In this paper, a Convolutional Neural Network (CNN) is adopted with image pre-processing to classify brain Magnetic Resonance (MR) images into four classes: glioma tumor, meningioma tumor, pituitary tumor and normal patients, is provided. We use a transfer le

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Hasan, Md Kamrul, Tanjum Tanha, Md Ruhul Amin, et al. "Cataract Disease Detection by Using Transfer Learning-Based Intelligent Methods." Computational and Mathematical Methods in Medicine 2021 (December 8, 2021): 1–11. http://dx.doi.org/10.1155/2021/7666365.

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One of the most common visual disorders is cataracts, which people suffer from as they get older. The creation of a cloud on the lens of our eyes is known as a cataract. Blurred vision, faded colors, and difficulty seeing in strong light are the main symptoms of this condition. These symptoms frequently result in difficulty doing a variety of tasks. As a result, preliminary cataract detection and prevention may help to minimize the rate of blindness. This paper is aimed at classifying cataract disease using convolutional neural networks based on a publicly available image dataset. In this obse

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Gao, Yan, and Amin Rezaeipanah. "An Ensemble Classification Method Based on Deep Neural Networks for Breast Cancer Diagnosis." Inteligencia Artificial 26, no. 72 (2023): 160–77. http://dx.doi.org/10.4114/intartif.vol26iss72pp160-177.

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Advances in technology have led to advances in breast cancer screening by detecting symptoms that doctors have overlooked. In this paper, an automatic detection system for breast cancer cases based on Internet of Things (IoT) is proposed. First, using IoT technology, direct medical images are sent to the data repository after the suspicious person's visit through medical equipment equipped with IoT. Then, in order to help radiologists, interpret medical images as best as possible, we use four pre-trained convolutional neural network models including InceptionResNetV2, InceptionV3, VGG19 and Re

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Anilkumar, Chunduru, Robbi Jyothsna, Sattaru Vandana Sree, and E. Gothai. "Deep Learning-Based Yoga Posture Specification Using OpenCV and Media Pipe." Applied and Computational Engineering 8, no. 1 (2023): 80–86. http://dx.doi.org/10.54254/2755-2721/8/20230085.

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Yoga is a years discipline that calls for physical postures, mental focus, and deep breathing. Yoga practice can enhance stamina, power, serenity, flexibility, and wellbeing. Yoga is currently a well-liked type of exercise worldwide. The foundation of yoga is good posture. Even though yoga offers many health advantages, poor posture can lead to issues including muscle sprains and pains. People have become more interested in working online than in person during the last few years. People who are accustomed to internet life and find it difficult to find the time to visit yoga studios benefit fro

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Hasanaath, Ahmed Abul, Abdul Sami Mohammed, Ghazanfar Latif, Sherif E. Abdelhamid, Jaafar Alghazo, and Ahmed Abul Hussain. "Acute lymphoblastic leukemia detection using ensemble features from multiple deep CNN models." Electronic Research Archive 32, no. 4 (2024): 2407–23. http://dx.doi.org/10.3934/era.2024110.

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<abstract> <p>We presented a methodology for detecting acute lymphoblastic leukemia (ALL) based on image data. The approach involves two stages: Feature extraction and classification. Three state-of-the-art transfer learning models, InceptionResnetV2, Densenet121, and VGG16, were utilized to extract features from the images. The extracted features were then processed through a Global Average Pooling layer and concatenated into a flattened tensor. A linear support vector machine (SVM) classifier was trained and tested on the resulting feature set. Performance evaluation was conducte

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Korkmaz, Adem, Tarık Talan, Selahattin Koşunalp, and Teodor Iliev. "Comparison of deep learning models in automatic classification of coffee bean species." PeerJ Computer Science 11 (April 7, 2025): e2759. https://doi.org/10.7717/peerj-cs.2759.

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As one of the most widely consumed beverages worldwide, coffee is characterized by its diverse flavor profiles and complex production processes. In this study, deep learning-based image processing techniques are employed for the automatic classification of coffee bean species with high accuracy. To achieve this, images of three different coffee bean species (Starbucks Pike Place, Espresso, and Kenya) were classified using five CNN-based models: Xception, DenseNet201, InceptionV3, InceptionResNetV2, and DenseNet121. The dataset comprises 1,554 coffee bean images. Cross-validation was applied to

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Al-Shargabi, Amal A., Jowharah F. Alshobaili, Abdulatif Alabdulatif, and Naseem Alrobah. "COVID-CGAN: Efficient Deep Learning Approach for COVID-19 Detection Based on CXR Images Using Conditional GANs." Applied Sciences 11, no. 16 (2021): 7174. http://dx.doi.org/10.3390/app11167174.

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COVID-19, a novel coronavirus infectious disease, has spread around the world, resulting in a large number of deaths. Due to a lack of physicians, emergency facilities, and equipment, medical systems have been unable to treat all patients in many countries. Deep learning is a promising approach for providing solutions to COVID-19 based on patients’ medical images. As COVID-19 is a new disease, its related dataset is still being collected and published. Small COVID-19 datasets may not be sufficient to build powerful deep learning detection models. Such models are often over-fitted, and their pr

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Mahendra, Randa, Eka Angga Laksana, and Sukenda Sukenda. "Pendekatan Transfer Learning dengan InceptionResNetV2 dan Augmentasi MixUp untuk Peningkatan Klasifikasi Tumor Otak." Jurnal Algoritma 22, no. 1 (2025): 161–72. https://doi.org/10.33364/algoritma/v.22-1.2194.

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Diagnosis tumor otak seperti Glioma, Meningioma, dan Pituitary melalui MRI masih menghadapi tantangan seperti ketergantungan pada interpretasi manual, waktu evaluasi yang lama, dan potensi kesalahan manusia. Untuk mengatasi hal tersebut, pendekatan berbasis deep learning menawarkan solusi yang efisien dan akurat. Penelitian ini bertujuan mengembangkan model klasifikasi tumor otak berbasis deep learning menggunakan arsitektur InceptionResNetV2 dengan teknik augmentasi MixUp untuk meningkatkan akurasi dan generalisasi model. Model dilatih pada 7.023 citra MRI (Glioma: 1.621, Meningioma: 1.645, P

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Chada, Govind. "Machine Learning Models for Abnormality Detection in Musculoskeletal Radiographs." Reports 2, no. 4 (2019): 26. http://dx.doi.org/10.3390/reports2040026.

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Increasing radiologist workloads and increasing primary care radiology services make it relevant to explore the use of artificial intelligence (AI) and particularly deep learning to provide diagnostic assistance to radiologists and primary care physicians in improving the quality of patient care. This study investigates new model architectures and deep transfer learning to improve the performance in detecting abnormalities of upper extremities while training with limited data. DenseNet-169, DenseNet-201, and InceptionResNetV2 deep learning models were implemented and evaluated on the humerus a

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Al-ahmadi, Roaa, Hatoon Al-ghamdi, and Lobna Hsairi. "Classification of Diabetic Retinopathy by Deep Learning." International Journal of Online and Biomedical Engineering (iJOE) 20, no. 01 (2024): 74–88. http://dx.doi.org/10.3991/ijoe.v20i01.45247.

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Diabetic retinopathy (DR), which is a leading cause of adult blindness, primarily affects individuals with diabetes. The manual diagnosis of DR, with the assistance of an ophthalmologist, has proven to be a time-consuming and challenging process. Late detection of DR is a significant factor contributing to the progression of the disease. To address this issue, the present study utilizes deep learning (DL) and transfer learning algorithms to analyze different stages of DR and precisely detect the condition. Using a large dataset comprising approximately 60,000 images, this study employs ResNet-

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Radočaj, Petra, and Goran Martinović. "Interpretable Deep Learning for Pediatric Pneumonia Diagnosis Through Multi-Phase Feature Learning and Activation Patterns." Electronics 14, no. 9 (2025): 1899. https://doi.org/10.3390/electronics14091899.

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Pediatric pneumonia remains a critical global health challenge requiring accurate and interpretable diagnostic solutions. Although deep learning has shown potential for pneumonia recognition on chest X-ray images, gaps persist in understanding model interpretability and feature learning during training. We evaluated four convolutional neural network (CNN) architectures, i.e., InceptionV3, InceptionResNetV2, DenseNet201, and MobileNetV2, using three approaches—standard convolution, multi-scale convolution, and strided convolution—all incorporating the Mish activation function. Among the tested

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Xie, Yuge, Jishi Zheng, Aozhuo Gou, Farhan Sattar, and Lyuchao Liao. "Log End Face Feature Extraction and Matching Method Based on Swin Transformer V2." Forests 16, no. 1 (2025): 124. https://doi.org/10.3390/f16010124.

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This study proposes a novel log end face feature extraction and matching method based on Swin Transformer V2, aiming to address limitations in accuracy and speed faced by traditional deep learning models, like InceptionResNetV2 and Vision Transformer. Accurate log identification is crucial for forestry and wood supply chain management, especially given the growing reliance on timber imports to meet industrial demands in construction, furniture manufacturing, and paper production. Our dataset comprises images of coniferous timber, specifically Scots pine (Pinus sylvestris L.), reflecting its si

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Cinar, Ilkay. "Deep Learning and LSTM Integration for Analyzing Driver Behaviors." Turkish Journal of Mathematics and Computer Science 17, no. 1 (2025): 191–211. https://doi.org/10.47000/tjmcs.1644390.

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Real-time detection of driver behaviors, fundamental for autonomous vehicles, is crucial for preventing accidents and enhancing traffic safety. Traditional methods, relying on manual observations or sensor-based monitoring, are increasingly being replaced by automated solutions using machine learning and computer vision technologies. This study aims to improve the classification of driver behaviors through the integration of deep learning models with LSTM layers. A multi-class driver behavior dataset, including images of safe driving, phone conversations, texting, turning, and other distractio

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Galih Purbo Danu Kisowo. "Perbandingan Akurasi CNN dan SVM Untuk Deteksi dan Klasifikasi Aktivitas Merokok." Router : Jurnal Teknik Informatika dan Terapan 2, no. 3 (2024): 48–55. http://dx.doi.org/10.62951/router.v2i3.145.

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This study compares the performance of Convolutional Neural Network (CNN) and Support Vector Machine (SVM) algorithms in detecting and classifying smoking activities. Using an image dataset containing two classes, Smoking and Non-Smoking, this research implements transfer learning using the InceptionResNetV2 model for CNN and the SVM method. Evaluation results show that CNN has higher accuracy compared to SVM in detecting smoking activities. This research contributes to the development of surveillance systems for smoke-free areas in smart cities.

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Silva, Luan Oliveira, Leandro dos Santos Araújo, Victor Ferreira Souza, Raimundo Matos Barros Neto, and Adam Santos. "Comparative Analysis of Convolutional Neural Networks Applied in the Detection of Pneumonia Through X-Ray Images of Children." Learning and Nonlinear Models 18, no. 2 (2021): 4–15. http://dx.doi.org/10.21528/lnlm-vol18-no2-art1.

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Pneumonia is one of the most common medical problems in clinical practice and is the leading fatal infectious disease worldwide. According to the World Health Organization, pneumonia kills about 2 million children under the age of 5 and is constantly estimated to be the leading cause of infant mortality, killing more children than AIDS, malaria, and measles combined. A key element in the diagnosis is radiographic data, as chest x-rays are routinely obtained as a standard of care and can aid to differentiate the types of pneumonia. However, a rapid radiological interpretation of images is not a

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Ali, Ahmed, Arshad Iqbal, Sohail Khan, Naveed Ahmad, and Sajid Shah. "A two-phase transfer learning framework for gastrointestinal diseases classification." PeerJ Computer Science 10 (December 19, 2024): e2587. https://doi.org/10.7717/peerj-cs.2587.

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Gastrointestinal (GI) disorders are common and often debilitating health issues that affect a significant portion of the population. Recent advancements in artificial intelligence, particularly computer vision algorithms, have shown great potential in detecting and classifying medical images. These algorithms utilize deep convolutional neural network architectures to learn complex spatial features in images and make predictions for similar unseen images. The proposed study aims to assist gastroenterologists in making more efficient and accurate diagnoses of GI patients by utilizing its two-pha

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Prachiti Pimple. "Multi-Dataset Evaluation of Hybrid Models for Brain Tumor Diagnosis." Journal of Information Systems Engineering and Management 10, no. 1s (2024): 332–45. https://doi.org/10.52783/jisem.v10i1s.126.

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Brain tumor detection is the identification and categorization of aberrant brain tissues using methods like MRI for tumor diagnosis and tracking. This sophisticated technique uses deep learning to analyze images, resulting in precise early detection and treatment. This study uses hybrid architectures for different deep-learning applications to offer a comprehensive hybridization strategy with promising prospects for improving the diagnostic precision of images obtained for medical diagnostics. In this study, it employs three separate datasets, previously known as Brain MRI images, Br35H and Br

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Rho, Jinhyung, Sung-Min Shin, Kyoungsun Jhang, et al. "Deep learning-based diagnosis of feline hypertrophic cardiomyopathy." PLOS ONE 18, no. 2 (2023): e0280438. http://dx.doi.org/10.1371/journal.pone.0280438.

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Feline hypertrophic cardiomyopathy (HCM) is a common heart disease affecting 10–15% of all cats. Cats with HCM exhibit breathing difficulties, lethargy, and heart murmur; furthermore, feline HCM can also result in sudden death. Among various methods and indices, radiography and ultrasound are the gold standards in the diagnosis of feline HCM. However, only 75% accuracy has been achieved using radiography alone. Therefore, we trained five residual architectures (ResNet50V2, ResNet152, InceptionResNetV2, MobileNetV2, and Xception) using 231 ventrodorsal radiographic images of cats (143 HCM and 8

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Diego Martínez, R., JD Cano de la Cruz, MI Sánchez Sánchez, LA Vázquez Pedreño, and M. Jiménez Pérez. "Development and training of a convolutional neural network for the detection of esophagitis in endoscopic images." Revista Andaluza de Patología Digestiva 48, no. 2 (2025): 46–50. https://doi.org/10.37352/2025482.1.

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Abstract Introduction and Objectives: digestive endoscopy provides a direct evaluation of the gastrointestinal tract, although inter-operator variability can limit its precision. This study aimed to develop a convolutional neural network (CNN) based on InceptionResNetV2, tailored for the automated detection of esophagitis in endoscopic images, with the objective of improving diagnostic accuracy and optimizing clinical workflow. Materials and Methods: the model was implemented using Python, Keras, and TensorFlow on Google Colab Pro with an Nvidia A100 GPU. Starting from the InceptionResNetV2 ar

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