Image Captioning using Google's Inception-resnet-v2 and Recurrent Neural Network

Bhatia, Yajurv; Bajpayee, Aman; Raghuvanshi, Deepanshu; Mittal, Himanshu

doi:10.1109/ic3.2019.8844921

Cited by 40 publications

(14 citation statements)

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“…CNN architectures have been particularly used for image detection, segmentation and classification because images have a special spatial property in their formation, such as edges, textures, gradients, orientation and color [ 15 ]. Many deep learning architectures have been proposed for automatic pattern recognition, such as the Inception-ResNet-v2, Inception-v3, VGG19, ResNet-50, DenseNet-201, Xception and MobileNetV2 architectures, with different performances depending on the characteristics of the data [ 17 , 18 , 19 , 20 , 21 , 22 , 23 ]. These CNN architectures have enabled the development of human-like efficient machines in different domains of application [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Networks Using Enhanced Radiographs for Real-Time Detection of Sitophilus zeamais in Maize Grain

Silva

Barroso

et al. 2021

Foods

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The application of artificial intelligence (AI) such as deep learning in the quality control of grains has the potential to assist analysts in decision making and improving procedures. Advanced technologies based on X-ray imaging provide markedly easier ways to control insect infestation of stored products, regardless of whether the quality features are visible on the surface of the grains. Here, we applied contrast enhancement algorithms based on peripheral equalization and calcification emphasis on X-ray images to improve the detection of Sitophilus zeamais in maize grains. In addition, we proposed an approach based on convolutional neural networks (CNNs) to identity non-infested and infested classes using three different architectures; (i) Inception-ResNet-v2, (ii) Xception and (iii) MobileNetV2. In general, the prediction models developed based on the MobileNetV2 and Xception architectures achieved higher accuracy (≥0.88) in identifying non-infested grains and grains infested by maize weevil, with a correct classification from 0.78 to 1.00 for validation and test sets. Hence, the proposed approach using enhanced radiographs has the potential to provide precise control of Sitophilus zeamais for safe human consumption of maize grains. The proposed method can automatically recognize food contaminated with hidden storage pests without manual features, which makes it more reliable for grain inspection.

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Section: Introductionmentioning

confidence: 99%

Convolutional Neural Networks Using Enhanced Radiographs for Real-Time Detection of Sitophilus zeamais in Maize Grain

Silva

Barroso

et al. 2021

Foods

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“…The next stage consists of simultaneously convolving one input using different filter sizes for each convolution and then concatenating them. The next parts of the network repeat 10 or 20 times the inputs and the network uses dropout layers to make the filter values equal to 0 to avoid overfitting [42].…”

Section: Classificationmentioning

confidence: 99%

“…In this study, the convolutional neural network architectures VGG16 [40], VGG19 [41], Inception-ResNetV2 [42], InceptionV3 [43], and DenseNet201 [44] are explored in different experiments to extract the characteristics of the spectral images of coffee fruits in different stages of ripening to determine which of them achieves the best results compared with the traditional classification carried out by experts who evaluate the color tonalities present in the skin of the fruits at the moment of harvesting. For this purpose, 4 experiments were carried out, implementing the techniques of unbalance balancing, subsampling, oversampling, and weighting on the training data.…”

Section: Introductionmentioning

confidence: 99%

Coffee Maturity Classification Using Convolutional Neural Networks and Transfer Learning

et al. 2022

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“…A subset of the more than a million images in the ImageNet database was used to train this network. The Google Inception CNN model (Bhatia et al, 2019), which was initially created for the ImageNet Recognition Challenge, is now in its third iteration. Using Inception V3, we were able to reduce the output layer's dimensions to one, flatten it, and add a sigmoid layer for classification along with a fully connected layer with 1024 hidden units, Relu activation function, and a dropout rate of 0.4.…”

Section: Inception V3mentioning

confidence: 99%

A deep learning based framework for the classification of multi- class capsule gastroscope image in gastroenterologic diagnosis

et al. 2022

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Purpose: The purpose of this paper is to develop a method to automatic classify capsule gastroscope image into three categories to prevent high-risk factors for carcinogenesis, such as atrophic gastritis (AG). The purpose of this research work is to develop a deep learning framework based on transfer learning to classify capsule gastroscope image into three categories: normal gastroscopic image, chronic erosive gastritis images, and ulcer gastric image.Method: In this research work, we proposed deep learning framework based on transfer learning to classify capsule gastroscope image into three categories: normal gastroscopic image, chronic erosive gastritis images, and ulcer gastric image. We used VGG- 16, ResNet-50, and Inception V3 pre-trained models, fine-tuned them and adjust hyperparameters according to our classification problem.Results: A dataset containing 380 images was collected for each capsule gastroscope image category, and divided into training set and test set in a ratio of 70%, and 30% respectively, and then based on the dataset, three methods, including as VGG- 16, ResNet-50, and Inception v3 are used. We achieved highest accuracy of 94.80% by using VGG- 16 to diagnose and classify capsule gastroscopic images into three categories: normal gastroscopic image, chronic erosive gastritis images, and ulcer gastric image. Our proposed approach classified capsule gastroscope image with respectable specificity and accuracy.Conclusion: The primary technique and industry standard for diagnosing and treating numerous stomach problems is gastroscopy. Capsule gastroscope is a new screening tool for gastric diseases. However, a number of elements, including image quality of capsule endoscopy, the doctors’ experience and fatigue, limit its effectiveness. Early identification is necessary for high-risk factors for carcinogenesis, such as atrophic gastritis (AG). Our suggested framework will help prevent incorrect diagnoses brought on by low image quality, individual experience, and inadequate gastroscopy inspection coverage, among other factors. As a result, the suggested approach will raise the standard of gastroscopy. Deep learning has great potential in gastritis image classification for assisting with achieving accurate diagnoses after endoscopic procedures.

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Image Captioning using Google's Inception-resnet-v2 and Recurrent Neural Network

Cited by 40 publications

References 1 publication

Convolutional Neural Networks Using Enhanced Radiographs for Real-Time Detection of Sitophilus zeamais in Maize Grain

Convolutional Neural Networks Using Enhanced Radiographs for Real-Time Detection of Sitophilus zeamais in Maize Grain

Coffee Maturity Classification Using Convolutional Neural Networks and Transfer Learning

A deep learning based framework for the classification of multi- class capsule gastroscope image in gastroenterologic diagnosis

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