AIMC Topic: Convolutional Neural Networks

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Intelligent diagnosis model for chest X-ray images diseases based on convolutional neural network.

BMC medical imaging
To address misdiagnosis caused by feature coupling in multi-label medical image classification, this study introduces a chest X-ray pathology reasoning method. It combines hierarchical attention convolutional networks with a multi-label decoupling lo...

Detection of fasting blood sugar using a microwave sensor and convolutional neural network.

Scientific reports
Monitoring of fasting blood sugar (FBS) is a critical component in the diagnosis and management of diabetes, one of the most widespread chronic diseases globally. Microwave sensing-particularly through microstrip-based sensors-has recently gained att...

Automatic detection of orthodontically induced external root resorption based on deep convolutional neural networks using CBCT images.

Scientific reports
Orthodontically-induced external root resorption (OIERR) is among the most common risks in orthodontic treatment. Traditional OIERR diagnosis is limited by subjective judgement as well as cumbersome manual measurement. The research aims to develop an...

Lightweight convolutional neural networks using nonlinear Lévy chaotic moth flame optimisation for brain tumour classification via efficient hyperparameter tuning.

Scientific reports
Deep convolutional neural networks (CNNs) have seen significant growth in medical image classification applications due to their ability to automate feature extraction, leverage hierarchical learning, and deliver high classification accuracy. However...

Real-Time Detection of Trace Breath Isoprene Based on Circular Domain Spectral Reconstruction Filtering Combined with Convolutional Neural Network.

Analytical chemistry
The detection of trace isoprene in breath provides a noninvasive method for lung cancer diagnosis. However, the presence of interfering components and the parts per billion (ppb) concentration levels of isoprene in breath complicate detection. In thi...

Determination of the oral carcinoma and sarcoma in contrast enhanced CT images using deep convolutional neural networks.

Scientific reports
Oral cancer is a hazardous disease and a major cause of morbidity and mortality worldwide. The purpose of this study was to develop the deep convolutional neural networks (CNN)-based multiclass classification and object detection models for distingui...

A deep convolutional neural network-based novel class balancing for imbalance data segmentation.

Scientific reports
Retinal fundus images provide valuable insights into the human eye's interior structure and crucial features, such as blood vessels, optic disk, macula, and fovea. However, accurate segmentation of retinal blood vessels can be challenging due to imba...

Improved convolutional neural network for precise exercise posture recognition and intelligent health indicator prediction.

Scientific reports
This paper presents a novel framework for accurate exercise posture recognition and health indicator prediction based on improved convolutional neural networks. We propose a multi-scale feature fusion architecture incorporating spatiotemporal attenti...

Automatic melanoma detection using an optimized five-stream convolutional neural network.

Scientific reports
Melanoma is among the deadliest forms of malignant skin cancer, with the number of cases increasing dramatically worldwide. Its early and accurate diagnosis is crucial for effective treatment. However, automatic melanoma detection has several signifi...

Enhancing occluded and standard bird object recognition using fuzzy-based ensembled computer vision approach with convolutional neural network.

Scientific reports
Classifying bird species is essential for ecological study and biodiversity protection, currently, conventional approaches are frequently laborious and susceptible to mistakes. Convolutional Neural Networks (CNNs) provide a more reliable option for f...