scholarly journals Applying Transfer Learning Using DenseNet121 in Radiographic Image Classification: تطبيق التعلم بالنقل باستخدام شبكة DenseNet121 في تصنيف الصور الشعاعية

2021 ◽  
Vol 5 (4) ◽  
pp. 58-41
Author(s):  
Nahla Saeed Saad Aldeen, Yosser Mohammad Marwan Atassi Nahla Saeed Saad Aldeen, Yosser Mohammad Marwan Atassi
Keyword(s):  
Transfer Learning ◽  
Medical Community ◽  
Diagnostic Process ◽  
Diagnostic Model ◽  
Learning Technology ◽  
Radiographic Images ◽  
Large Group ◽  
Long Time ◽  
Practical Factors ◽  
Fully Connected

The study aims to apply one of the fully connected convolutional neural networks, DenseNet121 network, to a data sample that includes a large group of radiographs through transfer learning technology. Radiography technology is a very important technique in the medical community to detect diseases and abnormalities that may be present, but the interpretation of these images may take a long time and it is subject to error by radiologists who are exposed to external practical factors (such as fatigue resulting from working for long hours, or exhaustion, or thinking about other life matters). To assist radiologists, we have worked on developing a diagnostic model with the help of a deep learning technique to classify radiographic images into two classes: (Normal and Abnormal images), by transferring the selected deep convolutional neural network between a large group of available networks that we studied on the basis of the regions that possibly abnormalities provided by the radiologists for the study sample. We also studied the feasibility of using the well-known VGG16 model on the same data sample and its performance through transfer learning technology and compared its results with the results of the DenseNet121 network. At the end of the research, we obtained a set of good results, which achieved a high diagnostic accuracy of 87.5% in some studied cases, using the DenseNet121 network model, which is considered satisfactory results in the case studied compared to the performance of other models. As for the VGG16 model, it did not give any of the satisfactory results in this field, the accuracy of the classification did not exceed 55% in most cases, and in only two cases it reached about 60% and 62%. The model presented during the research - DenseNet121 model - can be used in the diagnostic process and help in obtaining accurate results in terms of diagnostic results. As for the VGG16 model, it does not give satisfactory results according to the results also obtained during the research, so it is excluded in this type of applications.

scholarly journals Diagnosis despite clinical ambiguity: physicians’ perspectives on the rise in Autism Spectrum disorder incidence

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Michael Davidovitch ◽  
Dorit Shmueli ◽  
Ran Shmuel Rotem ◽  
Aviva Mimouni Bloch
Keyword(s):  
Autism Spectrum Disorder ◽  
Autism Spectrum ◽  
Mental Illnesses ◽  
Spectrum Disorder ◽  
Medical Community ◽  
Categorical Variables ◽  
Diagnostic Process ◽  
Child Neurology ◽  
Chi Square ◽  
Child Psychiatrists

Abstract Background To provide insight on physicians’ perspectives concerning recent changes in the incidence and diagnostic process of Autism Spectrum Disorder (ASD) compared to other mental and neurodevelopmental disorders. Method A questionnaire was sent to 191 specialists in child neurology and child development, and 200 child psychiatrists in Israel. Information was collected on professional background, as well as on physicians’ opinions concerning the accuracy and rate of ASD diagnosis compared to that of cerebral palsy (CP), mental illness, and Attention Deficit Hyperactivity Disorder (ADHD). For each closed-ended question, a global chi-square test for categorical variables was performed. Results 115 (60.2%) of specialists in child neurology and development, and 59 (29.5%) of child psychiatrists responded. Most physicians (67.2%) indicated that there was a moderate/significant increase in the incidence of ASD, which was higher than similar responses provided for CP (2.9%, p < 0.01) and mental illnesses (14.4%, p < 0.01), and similar to responses provided for ADHD (70.1%, p = 0.56). 52.8% of physicians believed that in more than 10% of clinical assessments, an ASD diagnosis was given despite an inconclusive evaluation (CP: 8.6%, p < 0.01; mental illnesses: 25.8%, p = 0.03; ADHD: 68.4%, p = 0.03). Conclusion The clinicians perceive both ASD and ADHD as over-diagnosed disorders. The shared symptomology between ASD and other disorders, coupled with heightened awareness and public de-stigmatization of ASD and with the availability of ASD-specific services that are not accessible to children diagnosed with other conditions, might lead clinicians to over-diagnose ASD. It is advisable to adopt an approach in which eligibility for treatments is conditional on function, rather than solely on a diagnosis. The medical community should strive for accurate diagnoses and a continuous review of diagnostic criteria.

A Study of the Influence of Data Complexity and Similarity on Soft Biometrics Classification Performance in a Transfer Learning Scenario

2021 ◽  
Vol 18 (2) ◽  
pp. 56-65
Author(s):  
Marcelo Romero ◽  
◽  
Matheus Gutoski ◽  
Leandro Takeshi Hattori ◽  
Manassés Ribeiro ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Classification Performance ◽  
Fine Tuning ◽  
Similarity Metrics ◽  
Soft Biometrics ◽  
Final Layer ◽  
The One ◽  
Fully Connected ◽  
The Relationship ◽  
Fine Tune

Transfer learning is a paradigm that consists in training and testing classifiers with datasets drawn from distinct distributions. This technique allows to solve a particular problem using a model that was trained for another purpose. In the recent years, this practice has become very popular due to the increase of public available pre-trained models that can be fine-tuned to be applied in different scenarios. However, the relationship between the datasets used for training the model and the test data is usually not addressed, specially where the fine-tuning process is done only for the fully connected layers of a Convolutional Neural Network with pre-trained weights. This work presents a study regarding the relationship between the datasets used in a transfer learning process in terms of the performance achieved by models complexities and similarities. For this purpose, we fine-tune the final layer of Convolutional Neural Networks with pre-trained weights using diverse soft biometrics datasets. An evaluation of the performances of the models, when tested with datasets that are different from the one used for training the model, is presented. Complexity and similarity metrics are also used to perform the evaluation.

An Effective Video Surveillance System by using CNN for COVID-19

2022 ◽  
pp. 88-102
Author(s):  
Basetty Mallikarjuna ◽  
Anusha D. J. ◽  
Sethu Ram M. ◽  
Munish Sabharwal
Keyword(s):  
Deep Learning ◽  
Video Surveillance ◽  
Surveillance System ◽  
Video Surveillance System ◽  
Learning Technology ◽  
Proposed Model ◽  
The Social ◽  
Scripting Language ◽  
Python Programming ◽  
Fully Connected

An effective video surveillance system is a challenging task in the COVID-19 pandemic. Building a model proper way of wearing a mask and maintaining the social distance minimum six feet or one or two meters by using CNN approach in the COVID-19 pandemic, the video surveillance system works with the help of TensorFlow, Keras, Pandas, which are libraries used in Python programming scripting language used in the concepts of deep learning technology. The proposed model improved the CNN approach in the area of deep learning and named as the Ram-Laxman algorithm. The proposed model proved to build the optimized approach, the convolutional layers grouped as ‘Ram', and fully connected layers grouped as ‘Laxman'. The proposed system results convey that the Ram-Laxman model is easy to implement in the CCTV footage.

Transfer learning with convolutional neural networks for small sample size problem in machinery fault diagnosis

2019 ◽  
Vol 233 (14) ◽  
pp. 5131-5143 ◽  
Author(s):  
Dengyu Xiao ◽  
Yixiang Huang ◽  
Chengjin Qin ◽  
Zhiyu Liu ◽  
Yanming Li ◽  
...  
Keyword(s):  
Neural Networks ◽  
Fault Diagnosis ◽  
Sample Size ◽  
Transfer Learning ◽  
Small Sample Size ◽  
Small Sample ◽  
Diagnostic Model ◽  
Source Data ◽  
Target Data ◽  
Machinery Fault Diagnosis

Data-driven machinery fault diagnosis has gained much attention from academic research and industry to guarantee the machinery reliability. Traditional fault diagnosis frameworks are commonly under a default assumption: the training and test samples share the similar distribution. However, it is nearly impossible in real industrial applications, where the operating condition always changes over time and the quantity of the same-distribution samples is often not sufficient to build a qualified diagnostic model. Therefore, transfer learning, which possesses the capacity to leverage the knowledge learnt from the massive source data to establish a diagnosis model for the similar but small target data, has shown potential value in machine fault diagnosis with small sample size. In this paper, we propose a novel fault diagnosis framework for the small amount of target data based on transfer learning, using a modified TrAdaBoost algorithm and convolutional neural networks. First, the massive source data with different distributions is added to the target data as the training data. Then, a convolutional neural network is selected as the base learner and the modified TrAdaBoost algorithm is employed for the weight update of each training sample to form a stronger diagnostic model. The whole proposition is experimentally demonstrated and discussed by carrying out the tests of six three-phase induction motors under different operating conditions and fault types. Results show that compared with other methods, the proposed framework can achieve the highest fault diagnostic accuracy with inadequate target data.

scholarly journals Cloud Image Classification Method Based on Deep Convolutional Neural Network

2020 ◽  
Vol 38 (4) ◽  
pp. 740-746
Author(s):  
Fei Zhang ◽  
Jie Yan
Keyword(s):  
Image Classification ◽  
Transfer Learning ◽  
Training Model ◽  
Classification Method ◽  
Support Vector ◽  
Test Accuracy ◽  
Learning Technology ◽  
Deep Convolutional Neural Networks ◽  
Fitting In ◽  
The Cost

Compared with satellite remote sensing images, ground-based invisible images have limited swath, but featured in higher resolution, more distinct cloud features, and the cost is greatly reduced, conductive to continuous meteorological observation of local areas. For the first time, this paper proposed a high-resolution cloud image classification method based on deep learning and transfer learning technology for ground-based invisible images. Due to the limited amount of samples, traditional classifiers such as support vector machine can't effectively extract the unique features of different types of clouds, and directly training deep convolutional neural networks leads to over-fitting. In order to prevent the network from over-fitting, this paper proposed applying transfer learning method to fine-tune the pre-training model. The proposed network achieved as high as 85.19% test accuracy on 6-type cloud images classification task. The networks proposed in this paper can be applied to classify digital photos captured by cameras directly, which will reduce the cost of system greatly.

scholarly journals Transfer learning for hostel image classification

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Chanattra Ammatmanee ◽  
Lu Gan
Keyword(s):  
Image Classification ◽  
Transfer Learning ◽  
Data Augmentation ◽  
Image Database ◽  
Learning Ability ◽  
Learning Technology ◽  
Human Errors ◽  
Content Type ◽  
Training Time ◽  
Processing Steps

PurposeBecause of the fast-growing digital image collections on online platforms and the transfer learning ability of deep learning technology, image classification could be improved and implemented for the hostel domain, which has complex clusters of image contents. This paper aims to test the potential of 11 pretrained convolutional neural network (CNN) with transfer learning for hostel image classification on the first hostel image database to advance the knowledge and fill the gap academically, as well as to suggest an alternative solution in optimal image classification with less labour cost and human errors to those who manage hostel image collections.Design/methodology/approachThe hostel image database is first created with data pre-processing steps, data selection and data augmentation. Then, the systematic and comprehensive investigation is divided into seven experiments to test 11 pretrained CNNs which transfer learning was applied and parameters were fine-tuned to match this newly created hostel image dataset. All experiments were conducted in Google Colaboratory environment using PyTorch.FindingsThe 7,350 hostel image database is created and labelled into seven classes. Furthermore, its experiment results highlight that DenseNet 121 and DenseNet 201 have the greatest potential for hostel image classification as they outperform other CNNs in terms of accuracy and training time.Originality/valueThe fact that there is no existing academic work dedicating to test pretrained CNNs with transfer learning for hostel image classification and no existing hostel image-only database have made this paper a novel contribution.

scholarly journals Classification of MR Brain tumors with Deep Plain and Residual Feed forward CNNs through Transfer learning

2019 ◽  
Vol 8 (6) ◽  
pp. 1758-1763
Keyword(s):  
Transfer Learning ◽  
Early Stage ◽  
Complex Structure ◽  
Machine Learning Algorithms ◽  
Learning Models ◽  
Feed Forward ◽  
Sensitivity Specificity ◽  
Fully Connected ◽  
Mr Brain

Medical imaging plays an important role in the diagnosis of some critical diseases and further treatment process of patients. Brain is a central and most complex structure in the human body that works with billions of cells, which controls all other organ functioning. Brain tumours observed as uncontrolled abnormal cell growth in brain tissues. Classification of such cells in a early stage will increase the survival rate of the patient. Machine learning algorithms have contributed much in automation of such tasks. Further improvement in prediction rate is possible through deep learning models. In this paper presents experiments by deep transfer learning models on publicly available dataset for Brain tumour classification. Pre-trained plain and residual feed forward models such as Alexnet, VGG19, ResNet50, ResNet101 and GoogleNet are used for the purpose of feature extraction, Fully connected layers and softmax layer for classification is used commonly. The evaluation metrics Accuracy, Sensitivity, Specificity and F1-Score were computed.

scholarly journals The 160th anniversary of Henry Turner

2018 ◽  
Vol 6 (4) ◽  
pp. 110-116
Author(s):  
Alexey G. Baindurashvili ◽  
Igor D. Vysoschuk ◽  
Alla V. Ovechkina ◽  
Anna V. Zaletina ◽  
Alyona N. Melchenko ◽  
...  
Keyword(s):  
Medical Community ◽  
Orthopedic Treatment ◽  
Organizational Skills ◽  
Military Medical Academy ◽  
Orthopedic Surgeons ◽  
Medical Academy ◽  
Long Time ◽  
To Come ◽  
The Military ◽  
And Training

The year 2018 in the medical community was marked by the 160th anniversary of the birth of Henry Ivanovich Turner. The phenomenal energy of this person, his organizational skills, talent as a scientist and public figure, dedication, and finally, his humanism are admired to this day and will serve as a model for the education of future doctors for a long time. Happiness and at the same time hard work to be the first. Henry Ivanovich Turner had fully experienced this happiness and this work. He was the organizer and leader of the first Russia Department and Clinic of Orthopedics of the Military Medical Academy, the initiator of the first Society of Orthopedic Surgeons, and the founder and honorary director of the USSR’s first Institute for the Rehabilitation of Physically Disabled Children. Henry Turner was one of the first in Russia to raise questions of a disabled child, pointed out the need for a systematic struggle of the state with children’s disability, and urged to come to the aid of a crippled child, initially with orthopedic treatment performed in conjunction with the upbringing, education, and training of any profession. The article presents the biography of the outstanding person, one of the founders of Russian orthopedics, Henry Ivanovich Turner.

A domain adaptation model for early gear pitting fault diagnosis based on deep transfer learning network

2019 ◽  
Vol 234 (1) ◽  
pp. 168-182 ◽  
Author(s):  
Jialin Li ◽  
Xueyi Li ◽  
David He ◽  
Yongzhi Qu
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Transfer Learning ◽  
Working Conditions ◽  
Deep Neural Network ◽  
Domain Adaptation ◽  
Diagnostic Model ◽  
Target Domain ◽  
Vibration Signals ◽  
Learning Network

In recent years, research on gear pitting fault diagnosis has been conducted. Most of the research has focused on feature extraction and feature selection process, and diagnostic models are only suitable for one working condition. To diagnose early gear pitting faults under multiple working conditions, this article proposes to develop a domain adaptation diagnostic model–based improved deep neural network and transfer learning with raw vibration signals. A particle swarm optimization algorithm and L2 regularization are used to optimize the improved deep neural network to improve the stability and accuracy of the diagnosis. When using the domain adaptation diagnostic model for fault diagnosis, it is necessary to discriminate whether the target domain (test data) is the same as the source domain (training data). If the target domain and the source domain are consistent, the trained improved deep neural network can be used directly for diagnosis. Otherwise, the transfer learning is combined with improved deep neural network to develop a deep transfer learning network to improve the domain adaptability of the diagnostic model. Vibration signals for seven gear types with early pitting faults under 25 working conditions collected from a gear test rig are used to validate the proposed method. It is confirmed by the validation results that the developed domain adaptation diagnostic model has a significant improvement in the adaptability of multiple working conditions.

scholarly journals Exudate Detection for Diabetic Retinopathy Using Pretrained Convolutional Neural Networks

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-11 ◽  
Author(s):  
Muhammad Mateen ◽  
Junhao Wen ◽  
Nasrullah Nasrullah ◽  
Song Sun ◽  
Shaukat Hayat
Keyword(s):  
Neural Network ◽  
Diabetic Retinopathy ◽  
Transfer Learning ◽  
Region Of Interest ◽  
The Other ◽  
Previous Knowledge ◽  
Residual Network ◽  
Softmax Classifier ◽  
Fully Connected ◽  
Visual Geometry

In the field of ophthalmology, diabetic retinopathy (DR) is a major cause of blindness. DR is based on retinal lesions including exudate. Exudates have been found to be one of the signs and serious DR anomalies, so the proper detection of these lesions and the treatment should be done immediately to prevent loss of vision. In this paper, pretrained convolutional neural network- (CNN-) based framework has been proposed for the detection of exudate. Recently, deep CNNs were individually applied to solve the specific problems. But, pretrained CNN models with transfer learning can utilize the previous knowledge to solve the other related problems. In the proposed approach, initially data preprocessing is performed for standardization of exudate patches. Furthermore, region of interest (ROI) localization is used to localize the features of exudates, and then transfer learning is performed for feature extraction using pretrained CNN models (Inception-v3, Residual Network-50, and Visual Geometry Group Network-19). Moreover, the fused features from fully connected (FC) layers are fed into the softmax classifier for exudate classification. The performance of proposed framework has been analyzed using two well-known publicly available databases such as e-Ophtha and DIARETDB1. The experimental results demonstrate that the proposed pretrained CNN-based framework outperforms the existing techniques for the detection of exudates.

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