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Journal articles on the topic 'Resnet Fastai'

Author: Grafiati

Published: 5 June 2025

Last updated: 1 August 2025

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1

Priyanshu, Shrivastava, Singh Karan, and Pancham Ashish. "Classification of Grains and Quality Analysis using Deep Learning." International Journal of Engineering and Advanced Technology (IJEAT) 11, no. 1 (2021): 244–50. https://doi.org/10.35940/ijeat.A3213.1011121.

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There are various varieties of Rice and lentils. Price fabrication and adulteration have been some of the various issues faced by the consumers, farmers and wholesale retailers. Traditional methods for Identification of these similar types of grains and their quality analysis are crude and inaccurate. Methods were tried to implemented earlier but due to financial inability and low efficiency, they weren’t successful. To overcome this problem, the project proposes a method that uses a machine learning technique for identification and quality analysis of these grains. Rice and Lentils whic

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Vannala, Sai Durga, and Vineela K. "Fastai-Powered Lung Cancer Classification Model." International Journal for Research in Applied Science and Engineering Technology 11, no. 10 (2023): 146–49. http://dx.doi.org/10.22214/ijraset.2023.55952.

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Abstract: Utilising Fastai and PyTorch, a deep learning model for classifying lung cancer was created. The model, which is based on the ResNet-30 architecture, was trained using a dataset that included the classes "lung_aca," "lung_n," and "lung_scc." Rotation and scaling were added to the dataset as enhancements. The training and validation sets each had 30 samples, and the model attained a high level of accuracy. Loss plots were employed to display the outcomes and the Fastai library was utilised for simple model construction and training. The Fastai and PyTorch versions of this model have b

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Saxena, Pranshu, Sanjay Kumar Singh, Mamoon Rashid, Sultan S. Alshamrani, and Mrim M. Alnfiai. "Efficient deep learning model for classifying lung cancer images using normalized stain agnostic feature method and FastAI-2." PeerJ Computer Science 11 (May 27, 2025): e2903. https://doi.org/10.7717/peerj-cs.2903.

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Background Lung cancer has the highest global fatality rate, with diagnosis primarily relying on histological tissue sample analysis. Accurate classification is critical for treatment planning and patient outcomes. Methods This study develops a computer-assisted diagnosis system for non-small cell lung cancer histology classification, utilizing the FastAI-2 framework with a modified ResNet-34 architecture. The methodology includes stain normalization using LAB colour space for colour consistency, followed by deep learning-based classification. The proposed model is trained on the LC25000 datas

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Pazhanivelan, Sellaperumal, K. P. Ragunath, N. S. Sudarmanian, S. Satheesh, and P. Shanmugapriya. "Deep Learning-Based Multi-Class Pest and Disease Detection in Agricultural Fields." Journal of Scientific Research and Reports 31, no. 1 (2025): 538–46. https://doi.org/10.9734/jsrr/2025/v31i12797.

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Farmers and agricultural workers would manually inspect crops for signs of pests or use traps to monitor pest populations. The advent of deep learning algorithms such as vision transformers and FastAI ResNet has brought about a significant transformation in pest detection practices. These advanced algorithms leverage the capabilities of artificial intelligence to process vast amounts of data and learn intricate patterns associated with different pest species and their impact on crops. Unlike manual methods, deep learning algorithms can analyze large datasets quickly and accurately, leading to

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Zhao, Hongfei. "Automatic Deep Learning-based Histopathologic Image Classification." Journal of Computer Science and Artificial Intelligence 3, no. 2 (2025): 30–34. https://doi.org/10.54097/4m060j94.

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Histopathologic image analysis is a critical component in cancer diagnosis, yet traditional manual inspection methods are often time-consuming, subjective, and error-prone. This study presents a fully automated deep learning framework for the classification of histopathologic images stained with Hematoxylin and Eosin (H&E). Leveraging Convolutional Neural Networks (CNNs), particularly DenseNet and ResNet architectures, the proposed system integrates essential components such as data preprocessing, augmentation, hyperparameter optimization, and training automation using the fastai library.

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Ulu, Banu. "Comparison of deep learning methods in brain tumor diagnosis: High-performance classification with MRI data." Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering 67, no. 1 (2025): 59–73. https://doi.org/10.33769/aupse.1619837.

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Brain tumors are serious health problems that must be diagnosed accurately and in a timely manner in order to provide effective treatment. Magnetic resonance imaging (MRI) is widely used in the detection of brain tumors. The accuracy of MRI results depends on the expertise of the physician and usually requires confirmation with biopsy. In recent years, revolutionary developments in image processing and deep learning technologies have provided significant improvements in the diagnosis and classification of brain tumors using MRI. In this study, it is aimed to classify brain tumors accurately an

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A.S, HARISH, AAGASH SETHUPATHI N,, and VISHAL A.S. "CRICKET POSE PREDICTION USING DEEP LEARNING." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 10 (2023): 1–11. http://dx.doi.org/10.55041/ijsrem26048.

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Cricket has a massive global following and is ranked as the second most popular sport globally, with an estimated 2.5 billion fans. Batting requires quick decisions based on ball speed, trajectory, fielder positions, etc. Recently, computer vision and machine learning techniques have gained attention as potential tools to predict cricket strokes played by batters. This study presents a cutting-edge approach to predicting batsman strokes using computer vision and machine learning. The study analyzes six strokes: pull, square-cut, cover drive, straight drive, flick, and sweep. The study uses the

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Hubens, Nathan, Matei Mancas, Bernard Gosselin, Marius Preda, and Titus Zaharia. "FasterAI: A Lightweight Library for Neural Networks Compression." Electronics 11, no. 22 (2022): 3789. http://dx.doi.org/10.3390/electronics11223789.

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FasterAI is a PyTorch-based library, aiming to facilitate the use of deep neural network compression techniques, such as sparsification, pruning, knowledge distillation, or regularization. The library is built with the purpose of enabling quick implementation and experimentation. More particularly, compression techniques are leveraging callback systems of libraries, such as fastai and Pytorch Lightning to propose a user-friendly and high-level API. The main asset of FasterAI is its lightweight, yet powerful, simplicity of use. Indeed, because it has been developed in a very granular way, users

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Bedoya Mazo, Sebastian, John Fredy Ramírez Agudelo, and Jose Fernando Guarín Montoya. "Evaluation of two computer vision approaches for grazing dairy cow identification." Biotecnología en el Sector Agropecuario y Agroindustrial 22, no. 2 (2024): 43–56. https://doi.org/10.18684/rbsaa.v22.n2.2024.2313.

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Computer vision is being used in Precision Livestock Farming to monitor and analyze animal health, behavior, and productivity. However, the implementation of these technologies faces technical challenges that require collaboration between farmers, researchers, and technology providers. In this study, the performance of two different grazing dairy cow identification approaches was compared using a ResNet-based computer vision model. The first approach consisted of image classification, while the second approach was based on the comparison of features or embeddings. The YOLOv5 model was used to

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J, Srilatha, S. Subashini T, and Vaidehi K. "Solid Waste Detection and Recognition using Faster RCNN." Indian Journal of Science and Technology 16, no. 42 (2023): 3778–85. https://doi.org/10.17485/IJST/v16i42.2005.

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Abstract <strong>Objective:</strong> To develop a two-stage object detection method based on convolutional neural networks (CNNs) to identify and classify solid waste, contributing to the creation of intelligent systems for society. <strong>Methods:</strong> The study utilizes a base network, ResNet 101, to generate convolution feature maps. In the first stage, a Region Proposal Network (RPN) is created on top of these convolution features, producing 256-dimensional feature vectors, objectness scores, and bounding rectangles for different anchor boxes. In the next stage, the reg

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Bayu, Theopilus. "Identifikasi Citra Plasmodium Guna Mendeteksi Parasit Penyakit Malaria dengan Menggunakan Algorithm Convolutional Neural Network." Science Tech: Jurnal Ilmu Pengetahuan dan Teknologi 8, no. 2 (2022): 25–35. http://dx.doi.org/10.30738/st.vol8.no2.a12796.

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Malaria merupakan jenis penyakit yang disebabkan oleh sel protozoa yang dinamakan Plasmodium, dalam perkembangannya sel Plasmodium akan memasuki dan menghancurkan sel-sel darah merah. Deteksi penyakit malaria merupakan sesuatu yang sulit karena membutuhkan waktu yang cukup lama. Parasit malaria dapat di lakukan identifikasi dengan menggunakan microscope untuk melihat apakah ada atau tidaknya Plasmodium dalam sel darah merah. Akan tetapi metode ini sangat bergantung pada kualitas dari microscope. Dikembangkan sebuah model computer vision dengan menggunakan CNN (convolutional neural network). Mo

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Andre, Citro Febriliyan Lanyak, Prasetiadi Agi, Budi Widodo Haris, Hisyam Ghani Muhammad, and Athallah Abiyan. "Dental caries detection using faster region-based convolutional neural network with residual network." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 2 (2024): 2027–35. https://doi.org/10.11591/ijai.v13.i2.pp2027-2035.

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Dental caries is the highest prevalent dental disease in the world by 2022. Caries can be stopped by early detection of patients through efficient screening. Previously, there have been several methods used to detect caries such as single shot multibox detector (SSD), faster region-based convolutional neural network (Faster R-CNN) and you only look once (YOLO). This research aims to develop accurate dental caries detection using Faster R-CNN. Using a dataset collected from scraping on the internet, this research is started by creating an original dataset consisting of 81 base images which are

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Scheid, John. "Myth, cult and reality in Ovid's Fasti." Proceedings of the Cambridge Philological Society 38 (1993): 118–31. http://dx.doi.org/10.1017/s0068673500001644.

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The relation of Ovid's Fasti to Roman religion has been discussed many times, but has never been dealt with properly. Despite a radical re-evaluation, in recent years, of Ovid's literary sophistication, scholars of Roman religion still tend to offer a negative judgment on Ovid's value. Criticisms have been varied. Ovid's unseriousness – frivolous tales in a context so solemn–has been considered exasperating. He has been accused (for example by Fr. Altheim) of lacking any religious sense whatsoever and of destroying respect for religion. Others, like Wissowa, have judged him to possess neither

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Ghuli, Poonam, Shashank B. N, and Athri G. Rao. "Development of framework for detecting smoking scene in video clips." Indonesian Journal of Electrical Engineering and Computer Science 13, no. 1 (2019): 22–26. https://doi.org/10.11591/ijeecs.v13.i1.pp22-26.

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According to Global Adult Tobacco Survey 2016-17, 61.9% of people are quitting tobacco. The reason was the warnings displayed on the product covers, video clips, and advertisments. The focus of this paper is to automate the process of displaying warning messages in video clips. This paper explains the development of a system to automatically detect the smoking scenes using image recognition approach in video clips and then add the warning message to the viewer. The approach aims to detect the cigarette object using Tensorflow’s object detection API. Tensorflow is an open source software

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Dat, Trinh Tan, Le Thien Vu Pham Cung, and Bao Pham The. "Comparing Mask R-CNN backbone architectures for human detection using thermal imaging." Comparing Mask R-CNN backbone architectures for human detection using thermal imaging 14, no. 4 (2024): 3962–70. https://doi.org/10.11591/ijece.v14i4.pp3962-3970.

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We introduce a method for detecting humans in thermal imaging using an end-to-end deep learning model. Our objective is to optimize the human detection process in thermal imaging by investigating the mask region-based convolutional neural network (Mask R-CNN). The model, an advancement of the faster region-based convolutional neural network (Faster R-CNN), not only captures bounding boxes encompassing human subjects but also delineates segmentation masks around them. Our investigation extends to the evaluation and comparison of various convolutional neural ne

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Mahajan Bhalchandra Paike, Harshal. "Comparative Study between Result of Temporalis Muscle Fascia and Tragal Cartilage Perichondrium as a Graft Material in Type 1 Tympanoplasty." International Journal of Science and Research (IJSR) 12, no. 4 (2023): 312–14. http://dx.doi.org/10.21275/sr23404084813.

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Brzezińska, O., K. Rychlicki-Kicior, and J. Makowska. "AB1377 FULLY AUTOMATIC ASSESSMENT OF NAIL FOLD CAPILLAROSCOPY SOFTWARE – PILOT STUDY." Annals of the Rheumatic Diseases 81, Suppl 1 (2022): 1793.3–1794. http://dx.doi.org/10.1136/annrheumdis-2022-eular.4859.

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BackgroundThe nail fold capillaroscopy is one of the basic techniques used in diagnosis and monitoring the course of connective tissue diseases, primarily a systemic sclerosis. However, the assessment of the capillary image is time-consuming and subjective, this makes it difficult for a detailed comparison of studies assessed by various physicians.ObjectivesThe aim of this study was to validate an automated software for classification the nail fold capillaroscopy as normal or pathological and counting the numerous of vessels on a millimetre.Methods100 correct and 100 pathological images were s

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Nishika, Manira, Monteiro Swelia, Alberto Tashya, Niasso Tracy, and Patil Supriya. "Geo-Landmark Recognition and Detection." International Journal of Innovative Technology and Exploring Engineering (IJITEE) 10, no. 7 (2021): 95–98. https://doi.org/10.35940/ijitee.G8983.0510721.

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The widespread use of smartphones and mobile data in the present-day society has exponentially led to the interaction with the physical world. The increase in the amount of image data in web and mobile applications makes image search slow and inaccurate. Landmark recognition, an image retrieval task, faces its challenges due to the uncommon structure it possesses, such as, buildings, cathedrals, castles or museums. These are shot from various angles which are often different from each other, for instance, the exterior and interior of a landmark. This paper makes use of a Convolutional Neural N

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Praveen, S. Phani, Parvathaneni Naga Srinivasu, Jana Shafi, Marcin Wozniak, and Muhammad Fazal Ijaz. "ResNet-32 and FastAI for diagnoses of ductal carcinoma from 2D tissue slides." Scientific Reports 12, no. 1 (2022). http://dx.doi.org/10.1038/s41598-022-25089-2.

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AbstractCarcinoma is a primary source of morbidity in women globally, with metastatic disease accounting for most deaths. Its early discovery and diagnosis may significantly increase the odds of survival. Breast cancer imaging is critical for early identification, clinical staging, management choices, and treatment planning. In the current study, the FastAI technology is used with the ResNet-32 model to precisely identify ductal carcinoma. ResNet-32 is having few layers comparted to majority of its counterparts with almost identical performance. FastAI offers a rapid approximation toward the o

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Demirdöğen, Filiz, Çağla Danacı, Seda Arslan Tuncer, Mustafa Akkuş, and Sevler Yıldız. "Deep Convolutional Neural Network Model for the Differential Diagnosis of Schizophrenia Using EEG Signals." Hitit Medical Journal, August 22, 2024. http://dx.doi.org/10.52827/hititmedj.1440548.

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Objective: One of the serious mental disorders in which people interpret reality in an abnormal situation is schizophrenia. A combination of extremely disordered thoughts, delusions, and hallucinations occurs due to schizophrenia, and the person's daily functions are seriously impaired due to this disease. For general cognitive activity analysis, electroencephalography signals are widely used as a low-resolution diagnostic tool. This study aimed to diagnose schizophrenia using the transfer learning method by including the EEGs of 73 patients diagnosed with schizophrenia, and 67 patients from t

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Yang, Dandi, Cristhian Martinez, Lara Visuña, Hardev Khandhar, Chintan Bhatt, and Jesus Carretero. "Detection and analysis of COVID-19 in medical images using deep learning techniques." Scientific Reports 11, no. 1 (2021). http://dx.doi.org/10.1038/s41598-021-99015-3.

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AbstractThe main purpose of this work is to investigate and compare several deep learning enhanced techniques applied to X-ray and CT-scan medical images for the detection of COVID-19. In this paper, we used four powerful pre-trained CNN models, VGG16, DenseNet121, ResNet50,and ResNet152, for the COVID-19 CT-scan binary classification task. The proposed Fast.AI ResNet framework was designed to find out the best architecture, pre-processing, and training parameters for the models largely automatically. The accuracy and F1-score were both above 96% in the diagnosis of COVID-19 using CT-scan imag

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Brzezińska, Olga Elżbieta, Krzysztof Andrzej Rychlicki-Kicior, and Joanna Samanta Makowska. "Automatic assessment of nailfold capillaroscopy software: a pilot study." Rheumatology, November 9, 2024. http://dx.doi.org/10.5114/reum/194040.

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IntroductionCapillaroscopy is a simple method of nailfold capillary imaging, used to diagnose diseases from the systemic sclerosis spectrum. However, the assessment of the capillary image is time-consuming and subjective. This makes it difficult to use for a detailed comparison of studies assessed by various physicians. This pilot study aimed to validate software used for automatic capillary counting and image classification as normal or pathological.Material and methodsThe study was based on the assessment of 200 capillaroscopic images obtained from patients suffering from systemic sclerosis

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"Does Cervicogenic Headache Result in the Presence of Neural Tension, and Does this Affect the Position and Mobility of Atlas?" Medical & Clinical Research 6, no. 1 (2021). http://dx.doi.org/10.33140/mcr.06.01.02.

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Background: Cervicogenic headaches (CGH) can be the result of dysfunction of the upper cervical spine. Due to the soft tissue connection between muscle, cervical fascia, and dura, this region might contribute to the development of CGH. Objective: Evaluate if subjects with CGH have neural tension signs. The secondary objective was to investigate if a correlation between the position and mobility of atlas and cervicogenic headaches exists. Methods: 60 Subjects were recruited. Self-reported outcome measures, passive neck flexion rotation test, upper limb tension test (ULTT), slump test, and strai

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