Deep Learning For Automatic Classification Research Articles

Deep Learning for Automatic Classification of Fruits and Vegetables: Evaluation from the Perspectives of Efficiency and Accuracy

Within the agricultural domain, accurately categorizing the freshness levels of fruits and vegetables holds immense significance, as this classification enables early detection of spoilage and allows for appropriate grouping of products based on their intended export destinations. These processes necessitate a system capable of meticulously classifying fruits and vegetables while minimizing labor expenditures. The current study concentrates on developing an advanced model that can effectively categorize the freshness status of each fruit and vegetable as 'good,' 'medium,' or 'spoiled.' To achieve this objective, various artificial intelligence models, including CNN, AlexNet, ResNet50, GoogleNet, VGG16, and EfficientB3, have been implemented, attaining remarkable success rates of 99.75%, 97.97%, 96.71%, 99.49%, 98.75%, and 99.81%, respectively.

Combining Handcrafted Features and Deep Learning for Automatic Classification of Lung Cancer on CT Scans

Combining Handcrafted Features and Deep Learning for Automatic Classification of Lung Cancer on CT Scans

Deep Learning for Automatic Classification of Identification Documents

This paper presents a general approach for identification documents classification using deep learning models. Our study gives an explanation of the main steps that need to be followed in order to implement a classification deep learning model. We have used convolution neural networks to extract features from raw image pixels on private datasets of identification documents. The implemented models use different techniques to preprocess the images in order to improve the classification performance on the test dataset and also techniques that can offer a better generalization of the models on the classification task. The experiments demonstrate that the training time-efficiency and accuracy of the models depends on the size, numbers of the pattern for each category and type of the image preprocessing. Various techniques of optimization have been applied to improve the model’s performance and as a result we achieved the best classification accuracy of 90.4% on the test dataset.

Natya Shastra: Deep Learning for Automatic Classification of Hand Mudra in Indian Classical Dance Videos

Natya Shastra: Deep Learning for Automatic Classification of Hand Mudra in Indian Classical Dance Videos

Automatic Classification of Red Blood Cell Morphology Based on Quantitative Phase Imaging

Classification of the morphology of red blood cells (RBCs) plays an extremely important role in evaluating the quality of long-term stored blood, as RBC storage lesions such as transformation of discocytes to echinocytes and then to spherocytes may cause adverse clinical effects. Most RBC segmentation and classification methods, limited by interference of staining procedures and poor details, are based on traditional bright field microscopy. In the present study, quantitative phase imaging (QPI) technology was combined with deep learning for automatic classification of RBC morphology. QPI can be used to observe unstained RBCs with high spatial resolution and phase information. In deep learning based on phase information, boundary curvature is used to reduce inadequate learning for preliminary screening of the three shapes of unstained RBCs. The model accuracy was 97.3% for the stacked sparse autoencoder plus Softmax classifier. Compared with the traditional convolutional neural network, the developed method showed a lower misclassification rate and less processing time, especially for RBCs with more discocytes. This method has potential applications in automatically evaluating the quality of long-term stored blood and real-time diagnosis of RBC-related diseases.

Supplementing a Web-based Exposure Estimation System with Deep Learning for Automatic Classification of CT Images to Increase the Efficiency of Effective Dose Estimation

Supplementing a Web-based Exposure Estimation System with Deep Learning for Automatic Classification of CT Images to Increase the Efficiency of Effective Dose Estimation

Collective view: mapping Sargassum distribution along beaches.

The atypical arrival of pelagic Sargassum to the Mexican Caribbean beaches has caused considerable economic and ecological damage. Furthermore, it has raised new challenges for monitoring the coastlines. Historically, satellite remote-sensing has been used for Sargassum monitoring in the ocean; nonetheless, limitations in the temporal and spatial resolution of available satellite platforms do not allow for near real-time monitoring of this macro-algae on beaches. This study proposes an innovative approach for monitoring Sargassum on beaches using Crowdsourcing for imagery collection, deep learning for automatic classification, and geographic information systems for visualizing the results. We have coined this collaborative process “Collective View”. It offers a geotagged dataset of images illustrating the presence or absence of Sargassum on beaches located along the northern and eastern regions in the Yucatan Peninsula, in Mexico. This new dataset is the largest of its kind in surrounding areas. As part of the design process for Collective View, three convolutional neural networks (LeNet-5, AlexNet and VGG16) were modified and retrained to classify images, according to the presence or absence of Sargassum. Findings from this study revealed that AlexNet demonstrated the best performance, achieving a maximum recall of 94%. These results are good considering that the training was carried out using a relatively small set of unbalanced images. Finally, this study provides a first approach to mapping the Sargassum distribution along the beaches using the classified geotagged images and offers novel insight into how we can accurately map the arrival of algal blooms along the coastline.

Supplementing a Web-based Exposure Estimation System with Deep Learning for Automatic Classification of CT Images to Increase the Efficiency of Effective Dose Estimation

Web-based exposure estimation systems are advantageous for estimating exposure doses for computed tomography (CT) scans. However, such systems depend on the imaging conditions of the slices, and a considerable amount of time and effort is needed to select the slices and extract their imaging conditions from the relevant CT volume data. In this study, we used a convolutional neural network (CNN) to automatically classify specific slices from available CT volume data for use by a Web-based exposure estimation system. We also proposed a method to automatically obtain the imaging conditions of these classified slices. The objective of this study was to improve the efficiency of effective dose estimation. We automatically classified specific slices from CT volume data using two different CNN architectures: VGG-16 and Xception. We organized the dataset into 5 categories corresponding to the contents of the specific slices. We also tested a 9-category version in which the slices were supplemented with their adjacent slices. We automatically obtained the imaging conditions from the DICOM tags of the specific slices that were classified from the CT volume data by the CNN and then estimated the effective exposure dose provided by the Web-based exposure estimation system. Using the 5-category dataset approach, the error in the effective exposure dose was 13% for VGG16 and 6% for Xception. When the 9-category approach was used, the error in the effective exposure dose was 0.8% for VGG16 and 0.6% for Xception. In both the architectures, less than 5 minutes was needed in the classification of the specific slices, followed by the extraction of their imaging conditions; however, VGG16 required the shortest processing time. By supplementing a Web-based exposure estimation system with a CNN and adopting our proposed method, we were able to improve the efficiency of effective dose estimation.

Automatic classification of grouper species by their sounds using deep neural networks.

In this paper, the effectiveness of deep learning for automatic classification of grouper species by their vocalizations has been investigated. In the proposed approach, wavelet denoising is used to reduce ambient ocean noise, and a deep neural network is then used to classify sounds generated by different species of groupers. Experimental results for four species of groupers show that the proposed approach achieves a classification accuracy of around 90% or above in all of the tested cases, a result that is significantly better than the one obtained by a previously reported method for automatic classification of grouper calls.