AIMC Topic: Neural Networks, Computer

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CBG-Net: Cross-modality and cross-scale balance network with global semantics for multi-modal 3D object detection.

Neural networks : the official journal of the International Neural Network Society
Multi-modal 3D object detection is instrumental in identifying and localizing objects within 3D space. It combines RGB images from cameras and point-clouds data from lidar sensors, serving as a fundamental technology for autonomous driving applicatio...

Sustainable utilization of FeO-modified activated lignite for aqueous phosphate removal and ANN modeling.

Environmental research
Lignites are widely available and cost-effective in many countries. Sustainable methods for their utilization drive innovation, potentially advancing environmental sustainability and resource efficiency. In the present study, FeO (∼25.1 nm) supported...

A position-enhanced sequential feature encoding model for lung infections and lymphoma classification on CT images.

International journal of computer assisted radiology and surgery
PURPOSE: Differentiating pulmonary lymphoma from lung infections using CT images is challenging. Existing deep neural network-based lung CT classification models rely on 2D slices, lacking comprehensive information and requiring manual selection. 3D ...

Efficient multi-stage feedback attention for diverse lesion in cancer image segmentation.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
In the domain of Computer-Aided Diagnosis (CAD) systems, the accurate identification of cancer lesions is paramount, given the life-threatening nature of cancer and the complexities inherent in its manifestation. This task is particularly arduous due...

Identification and diagnosis of schizophrenia based on multichannel EEG and CNN deep learning model.

Schizophrenia research
This paper proposes a high-accuracy EEG-based schizophrenia (SZ) detection approach. Unlike comparable literature studies employing conventional machine learning algorithms, our method autonomously extracts the necessary features for network training...

Automated Method for Intracranial Aneurysm Classification Using Deep Learning.

Sensors (Basel, Switzerland)
Intracranial aneurysm (IA) is now a common term closely associated with subarachnoid hemorrhage. IA is the bulging of a blood vessel caused by a weakening of its wall. This bulge can rupture and, in most cases, cause internal bleeding. In most cases,...

Artificial intelligence based diagnosis of sulcus: assesment of videostroboscopy via deep learning.

European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery
PURPOSE: To develop a convolutional neural network (CNN)-based model for classifying videostroboscopic images of patients with sulcus, benign vocal fold (VF) lesions, and healthy VFs to improve clinicians' accuracy in diagnosis during videostroboscop...

Seizure Detection of EEG Signals Based on Multi-Channel Long- and Short-Term Memory-Like Spiking Neural Model.

International journal of neural systems
Seizure is a common neurological disorder that usually manifests itself in recurring seizure, and these seizures can have a serious impact on a person's life and health. Therefore, early detection and diagnosis of seizure is crucial. In order to impr...

Mass detection in automated three dimensional breast ultrasound using cascaded convolutional neural networks.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
PURPOSE: Early detection of breast cancer has a significant effect on reducing its mortality rate. For this purpose, automated three-dimensional breast ultrasound (3-D ABUS) has been recently used alongside mammography. The 3-D volume produced in thi...