Optimization of CNN model for image classification

Dhouibi, Meriam; Salem, Ahmed Karim Ben; Saoud, Slim Ben

doi:10.1109/dts52014.2021.9497988

Cited by 8 publications

(4 citation statements)

References 7 publications

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“…Changing the image resolution is done so that the image has a uniform size. In addition, by changing the image size to a smaller size, it is hoped that the CNN process can work more optimally as stated by [24]. The new image resolution is applied at each stage of training, validation, and testing which affects the output of the CNN model.…”

Section: Methodsmentioning

confidence: 99%

Implementation of Spatial-Level Augmentation on Pneumonia Classification with Convolutional Neural Network

Saputra¹

2022

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Pneumonia is a disease with symptoms of difficulty breathing, fever, dry cough, and chest pain. As an indication that a person has COVID-19, it is necessary to immediately identify pneumonia by a doctor whether the lung X-ray image of the patient is classified as pneumonia or not. The importance of early diagnosis can reduce the risk of death in patients. Convolutional Neural Network is one of the fields of study in the realm of Computer Science that can perform image-based object classification. CNN can be used to identify pneumonia based on thorax images based on the features or features displayed on the image. One of the important elements in CNN is the amount of data that can be used for training, validation, and testing. Generally, the more data entered, the more learning material for the CNN system so that the system can classify more accurately. This study aims to measure the accuracy of the CNN model on thoracic-pneumonia images with spatial level augmentation changes. Image augmentation is implemented to increase image variance with initial data of 5856 images. The applied augmentations are Affine, Flip, Pixel Dropout, Random Size Crop, and Shift Scale Rotate. The stages of this research are manually grouping images, implementing augmentation on images, applying training-validation-testing on CNN, and analyzing the output results of the developed system. By using 5 types of augmentation, the dataset used as learning material can be increased up to 5x the original amount. From the research carried out, it was found that the Random-sized Crop type augmentation gave the highest accuracy value of 94.719% or an increase of 3.808% from the non-augmentation testing data. From this research, it is hoped that studies related to augmentation can be a reference regarding the type of augmentation process and its results in finding the CNN accuracy value, especially in the case of pneumonia classification

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Section: Methodsmentioning

confidence: 99%

Implementation of Spatial-Level Augmentation on Pneumonia Classification with Convolutional Neural Network

Saputra¹

2022

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“…Numerous efforts have been made to enhance the efficiency and accuracy of CNNs. For instance, to streamline the CNN model's architecture and reduce computational costs, [7] explored CNN optimization for image classification, determining the optimal topology (including the number of layers and neurons per layer). In another study by [8], InceptionNet was used to group and correlate text and images in Twitter posts by employing both InceptionNet and LSTM.…”

Section: Related Workmentioning

confidence: 99%

E-commerce image classification with transfer learning

Tantirapan,

Pojam,

Chunloy

et al. 2024

Fifth International Conference on Computer Vision and Computational Intelligence (CVCI 2024)

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A major e-commerce platform like Shopee offers thousands of macro categories for listing various products. Product price comparison shopping is a challenge for the e-commerce platform. Image classification helps customers accurately search for products from a large number of images, and it also helps them find the best price deals. To develop an effective image classification model for this use case, we utilized Convolutional Neural Networks (CNN) with transfer learning in our study. We sourced our image dataset from Kaggle (Shopee -Price Match Guarantee) and selected 645 ecommerce images from the top 20 classes, each containing the highest number of images. We chose MobileNetV2, EfficientNetB3, and InceptionV3 to compare the efficiency of pre-trained models. Our experimental results revealed that EfficientNetB3 and MobileNetV2 outperformed InceptionV3 in the validation set. Moreover, the time required to train all layers of EfficientNetB3 was approximately 8 times less than that of MobileNetV2.

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“…The limitation of this method is same as Srivastava et al [ 6 ] research which has a difficulty to train through a big dataset. Dhouibi [ 9 ] published a paper-entitled optimization of the CNN model for image classification. It is talking about topology optimization of CNN in terms of number of layers and the number of neurons per layer.…”

Section: Literature Reviewmentioning

confidence: 99%

Image Classification Based on Light Convolutional Neural Network Using Pulse Couple Neural Network

Rafidison

Ramafiarisona

Randriamitantsoa

et al. 2023

Computational Intelligence and Neuroscience

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Recently, most image classification studies solicit the intervention of convolutional neural networks because these DL-based classification methods generally outperform other methodologies with higher accuracy. However, this type of deep learning networks require many parameters and have a complex structure with multiple convolutional and pooling layers depending on the objective. These layers compute a large volume of data and it may impact the processing time and the performance. Therefore, this paper proposes a new method of image classification based on the light convolutional neural network. It consists of replacing the feature extraction layers of standard convolutional neural network with a single pulse coupled neural network by introducing the notion of foveation. This module provides the feature map of input image and the data compression using Discrete Wavelet Transform which is an optional step depending on the information quantity of this signature. The fully connected neural network, which has six hidden layers, classifies the image. With this technique, the computation time is reduced, and the network architecture is identical and simple independent of the type of dataset. The number of parameter is less than that in current research. The proposed method was validated with different dataset such as Caltech-101, Caltech-256, CIFAR-10, CIFAR-100, and ImageNet, and the accuracy reaches 92%, 90%, 99%, 94%, and 91%, respectively, which are better than the previous related works.

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Optimization of CNN model for image classification

Cited by 8 publications

References 7 publications

Implementation of Spatial-Level Augmentation on Pneumonia Classification with Convolutional Neural Network

Implementation of Spatial-Level Augmentation on Pneumonia Classification with Convolutional Neural Network

E-commerce image classification with transfer learning

Image Classification Based on Light Convolutional Neural Network Using Pulse Couple Neural Network

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