Comparative Analysis of Recent Architecture of Convolutional Neural Network

Saleem, Muhammad Asif; Senan, Norhalina; Wahid, Fazli; Aamir, Mohammad; Samad, Ali; Khan, Mukhtaj

doi:10.1155/2022/7313612

Cited by 29 publications

(16 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Pre trained network in deep learning is defined as the networks that are trained to extract most powerful features from the images and use these features to learn new tasks. In most of the cases, the network is trained with ImageNet database which is used in ImageNet Large-Scale Visual Recognition Challenge (ILSVRC) [23]. The networks is trained with large amount of images having a class of thousand.…”

Section: Pretrained Deep Learningmentioning

confidence: 99%

Pretrained CNN Architectures: A Detailed Analysis Using Bell Pepper Image Datasets

Mathew,

.M,

et al. 2023

Preprint

View full text Add to dashboard Cite

In the era of artificial intelligence, automation is becoming popular in every sector. The primary sector includes the agriculture sector. Farmers are facing problems such as the identification of diseases in their plants, lack of proper treatment for the disease, climatic changes that affect their yield, and low price for their crops. In this paper, we are mainly focusing on the disease identification of bell pepper plants using deep learning architectures such as Alex Net, google net, ResNet (18,50,101), and Vgg (16,19). We also focus on the detailed study of different pre-trained CNN architectures to analyze their performance and identify which architecture is more suitable for disease classification in bell pepper. This paper also helps bell pepper farmers to identify the disease with high accuracy compared to the traditional methods of disease identification. The new automation concept helps bell pepper framers to identify diseases with less time and effort, which makes their work easier. The identification of disease at an early stage with less effort will help the farmer to increase their yield. The paper will help to understand the performance of different pre-trained convolutional neural network architectures with and without augmentation of images and also compare the performance of the architectures. Based on these comparisons, it could find out that google net is more suitable for the classification of images in bell pepper as compared to other architectures with augmentation, and vgg19 was observed to be best for the classification of images without augmentation.

show abstract

Section: Pretrained Deep Learningmentioning

confidence: 99%

Pretrained CNN Architectures: A Detailed Analysis Using Bell Pepper Image Datasets

Mathew,

.M,

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Fully-connected layer is the last layer that functions as a classifier, so this layer is an important element of a convolutional neural network [24]. This layer generally uses a neural network that can be trained and can store the weight values of the training results.…”

Section: Fully-connected Layermentioning

confidence: 99%

Malware Image Classification Using Deep Learning InceptionResNet-V2 and VGG-16 Method

Didih Rizki Chandranegara,

Jafar Shodiq Djawas,

Faiq Azmi Nurfaizi

et al. 2023

join

View full text Add to dashboard Cite

Malware is intentionally designed to damage computers, servers, clients or computer networks. Malware is a general term used to describe any program designed to harm a computer or server. The goal is to commit a crime, such as gaining unauthorized access to a particular system, so as to compromise user security. Most malware still uses the same code to produce another different form of malware variants. Therefore, the ability to classify similar malware variant characteristics into malware families is a good strategy to stop malware. The research is useful for classifying malware on malware samples presented as bytemap grayscale images. The malware classification research focused on 25 malware classes with a total of 9,029 images from the Malimg dataset. This research implements the VGG-16 and InceptionResNet-V2 architectures by running 2 different scenarios, scenario 1 uses the original dataset and the other scenario uses the undersampled dataset. After building the model, each scenario will get an evaluation form such as accuracy, precision, recall, and f1-score. The highest score was obtained in scenario 2 on the VGG-16 method with a score of 94.8% and the lowest in scenario 2 on the InceptionResNet-V2 method with a score of 85.1%.

show abstract

“…This can be implemented by simulating human vision systems and human attention principles on image processing algorithms. Explainability is also one of the significant directions in the application of DL models as the deep architectures essentially act as black boxes and it is important for justifying the solutions provided by these architectures for human acceptability of the solution [41]. Other notable directions in the application of DL models in medical image classification and segmentation are given below:…”

Section: Future Directions In Application Of DL Models In Medical Ima...mentioning

confidence: 99%

A Systematic Study of Deep Learning Architectures for Analysis of Glaucoma and Hypertensive Retinopathy

M¹,

Prasad²,

Kulkarni³

et al. 2022

IJAIA

View full text Add to dashboard Cite

Deep learning models are applied seamlessly across various computer vision tasks like object detection, object tracking, scene understanding and further. The application of cutting-edge deep learning (DL) models like U-Net in the classification and segmentation of medical images on different modalities has established significant results in the past few years. Ocular diseases like Diabetic Retinopathy (DR), Glaucoma, Age-Related Macular Degeneration (AMD / ARMD), Hypertensive Retina (HR), Cataract, and dry eyes can be detected at the early stages of disease onset by capturing the fundus image or the anterior image of the subject’s eye. Early detection is key to seeking early treatment and thereby preventing the disease progression, which in some cases may lead to blindness. There is a plethora of deep learning models available which have established significant results in medical image processing and specifically in ocular disease detection. A given task can be solved by using a variety of models and or a combination of them. Deep learning models can be computationally expensive and deploying them on an edge device may be a challenge. This paper provides a comprehensive report and critical evaluation of the various deep learning architectures that can be used to segment and classify ocular diseases namely Glaucoma and Hypertensive Retina on the posterior images of the eye. This review also compares the models based on complexity and edge deployability.

show abstract

Comparative Analysis of Recent Architecture of Convolutional Neural Network

Cited by 29 publications

References 28 publications

Pretrained CNN Architectures: A Detailed Analysis Using Bell Pepper Image Datasets

Pretrained CNN Architectures: A Detailed Analysis Using Bell Pepper Image Datasets

Malware Image Classification Using Deep Learning InceptionResNet-V2 and VGG-16 Method

A Systematic Study of Deep Learning Architectures for Analysis of Glaucoma and Hypertensive Retinopathy

Contact Info

Product

Resources

About