Deep learning: Applications, architectures, models, tools, and frameworks: A comprehensive survey

Gheisari, Mehdi; Ebrahimzadeh, Fereshteh; Rahimi, Mohamadtaghi; Moazzamigodarzi, Mahdieh; Liu, Yang; Pramanik, Pijush Kanti Dutta; Heravi, Mohammad Ali; Mehbodniya, Abolfazl; Ghaderzadeh, Mustafa; Feylizadeh, Mohammad Reza; Kosari, Saeed

doi:10.1049/cit2.12180

Cited by 83 publications

(56 citation statements)

References 87 publications

(114 reference statements)

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“…It is one of the implementations of a neural network that has been widely used in image-based learning. Automatic extraction and selection of features are two of the main strengths of deep learning models [ 19 ]. CNN in particular is effective in extracting deep features.…”

Section: Methodsmentioning

confidence: 99%

Fungal Skin Disease Classification Using the Convolutional Neural Network

Nigat,

Sitote,

Gedefaw

2023

Journal of Healthcare Engineering

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Skin is the outer cover of our body, which protects vital organs from harm. This important body part is often affected by a series of infections caused by fungus, bacteria, viruses, allergies, and dust. Millions of people suffer from skin diseases. It is one of the common causes of infection in sub-Saharan Africa. Skin disease can also be the cause of stigma and discrimination. Early and accurate diagnosis of skin disease can be vital for effective treatment. Laser and photonics-based technologies are used for the diagnosis of skin disease. These technologies are expensive and not affordable, especially for resource-limited countries like Ethiopia. Hence, image-based methods can be effective in reducing cost and time. There are previous studies on image-based diagnosis for skin disease. However, there are few scientific studies on tinea pedis and tinea corporis. In this study, the convolution neural network (CNN) has been used to classify fungal skin disease. The classification was carried out on the four most common fungal skin diseases: tinea pedis, tinea capitis, tinea corporis, and tinea unguium. The dataset consisted of a total of 407 fungal skin lesions collected from Dr. Gerbi Medium Clinic, Jimma, Ethiopia. Normalization of image size, conversion of RGB to grayscale, and balancing the intensity of the image have been carried out. Images were normalized to three sizes: 120 × 120, 150 × 150, and 224 × 224. Then, augmentation was applied. The developed model classified the four common fungal skin diseases with 93.3% accuracy. Comparisons were made with similar CNN architectures: MobileNetV2 and ResNet 50, and the proposed model was superior to both. This study may be an important addition to the very limited work on the detection of fungal skin disease. It can be used to build an automated image-based screening system for dermatology at an initial stage.

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Section: Methodsmentioning

confidence: 99%

Fungal Skin Disease Classification Using the Convolutional Neural Network

Nigat,

Sitote,

Gedefaw

2023

Journal of Healthcare Engineering

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“…Each system has been compared and evaluated based on deep learning model, functions, year of publication, dataset, and performance. The recent trends of computer‐vision research community are based upon the use of deep learning models 12–14,15 …”

Section: Literature Reviewmentioning

confidence: 99%

“…The recent trends of computer-vision research community are based upon the use of deep learning models. [12][13][14]15 Juneja et al performed the analysis of already existing studies for retinal image classification as normal or abnormal. 16 Moreover, Classification of Glaucoma Network (CoG-Net) eliminated the requirement of handcrafted extraction of features.…”

Section: Comparison Of Deep Learning Models For Glaucoma Classificationmentioning

confidence: 99%

Deep learning based Glaucoma Network Classification (GNC) using retinal images

Kiyani,

Shehryar,

Khalid

et al. 2023

Int J Imaging Syst Tech

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The proposed deep learning framework for glaucoma classification addresses critical challenges of limited data and computational costs. Employing data augmentation and normalization techniques, the three‐stage model, utilizing InceptionV3 and ResNet50, achieves high training (99.3% ‐ 99.8%) and testing accuracy (91.6% ‐ 92.12%) on a dataset comprising 16,328 images from fused public datasets. This outperforms existing automated models. The approach leverages transfer learning and convolutional neural networks, showcasing its potential for accurate and timely glaucoma diagnosis. However, ongoing validation on diverse datasets and ethical considerations regarding fairness and transparency in medical applications remain essential. The model's reliability suggests its promising role in aiding early glaucoma detection, potentially averting irreversible vision impairment.

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“…Hinton et al [14] put forward deep learning for the first time, opening the door to the scientific research field of deep learning. The convolution neural network (CNN) is the most representative algorithm of deep learning technology, and the deep learning network model built using the CNN is also the direction that many scholars have continued to explore [15][16][17][18][19]. In 2012 at the ImageNet competition, Krizhevsky et al [20] proposed the AlexNet large convolutional neural network.…”

Section: Introductionmentioning

confidence: 99%

Bearing Fault Diagnosis Method Based on Deep Learning and Health State Division

Song

Wang

et al. 2023

Applied Sciences

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As a key component of motion support, the rolling bearing is currently a popular research topic for accurate diagnosis of bearing faults and prediction of remaining bearing life. However, most existing methods still have difficulties in learning representative features from the raw data. In this paper, the Xi’an Jiaotong University (XJTU-SY) rolling bearing dataset is taken as the research object, and a deep learning technique is applied to carry out the bearing fault diagnosis research. The root mean square (RMS), kurtosis, and sum of frequency energy per unit acquisition period of the short-time Fourier transform are used as health factor indicators to divide the whole life cycle of bearings into two phases: the health phase and the fault phase. This division not only expands the bearing dataset but also improves the fault diagnosis efficiency. The Deep Convolutional Neural Networks with Wide First-layer Kernels (WDCNN) network model is improved by introducing multi-scale large convolutional kernels and Gate Recurrent Unit (GRU) networks. The bearing signals with classified health states are trained and tested, and the training and testing process is visualized, then finally the experimental validation is performed for four failure locations in the dataset. The experimental results show that the proposed network model has excellent fault diagnosis and noise immunity, and can achieve the diagnosis of bearing faults under complex working conditions, with greater diagnostic accuracy and efficiency.

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Deep learning: Applications, architectures, models, tools, and frameworks: A comprehensive survey

Cited by 83 publications

References 87 publications

Fungal Skin Disease Classification Using the Convolutional Neural Network

Fungal Skin Disease Classification Using the Convolutional Neural Network

Deep learning based Glaucoma Network Classification (GNC) using retinal images

Bearing Fault Diagnosis Method Based on Deep Learning and Health State Division

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