AIMC Topic: Algorithms

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Hierarchical in-out fusion for incomplete multimodal brain tumor segmentation.

Scientific reports
Fusing multimodal data play a crucial role in accurate brain tumor segmentation network and clinical diagnosis, especially in scenarios with incomplete multimodal data. Existing multimodal fusion models usually perform intra-modal fusion at both shal...

AI-driven genetic algorithm-optimized lung segmentation for precision in early lung cancer diagnosis.

Scientific reports
Lung cancer remains the leading cause of cancer-related mortality worldwide, necessitating accurate and efficient diagnostic tools to improve patient outcomes. Lung segmentation plays a pivotal role in the diagnostic pipeline, directly impacting the ...

An optimized domain-specific shrimp detection architecture integrating conditional GAN and weighted ensemble learning.

Scientific reports
Deep learning primarily operates on images which contain hidden patterns that are quantified through pixel intensities. Deep learning is used to analyze the image patterns and to recognize the objects. The detection process includes the creation of l...

Multi channel fusion diffusion models for brain tumor MRI data augmentation.

Scientific reports
The early diagnosis of brain tumors is crucial for patient prognosis, and medical imaging techniques such as MRI and CT scans are essential tools for diagnosing brain tumors. However, high-quality medical image data for brain tumors is often scarce a...

Multi-scale fusion semantic enhancement network for medical image segmentation.

Scientific reports
The application of sophisticated computer vision techniques for medical image segmentation (MIS) plays a vital role in clinical diagnosis and treatment. Although Transformer-based models are effective at capturing global context, they are often ineff...

Cervical cancer prediction using machine learning models based on routine blood analysis.

Scientific reports
Cervical cancer (CC) is the fourth most common cancer among women globally. The key to preventing and treating CC is early detection, diagnosis, and treatment. This study aimed to develop an interpretable model for predicting CC risk using routine bl...

The construction of HMME-PDT efficacy prediction model for port-wine stain based on machine learning algorithms.

Scientific reports
Hematoporphyrin monomethyl ether-photodynamic therapy (HMME-PDT) is a safe and effective treatment for port-wine stain (PWS). Comprehensive methods for predicting HMME-PDT efficacy based on clinical factors are lacking. This study aims to develop and...

End-to-end feature fusion for jointly optimized speech enhancement and automatic speech recognition.

Scientific reports
Speech enhancement (SE) and automatic speech recognition (ASR) in real-time processing involve improving the quality and intelligibility of speech signals on the fly, ensuring accurate transcription as the speech unfolds. SE eliminates unwanted backg...

A deep dive into artificial intelligence with enhanced optimization-based security breach detection in internet of health things enabled smart city environment.

Scientific reports
Internet of Health Things (IoHT) plays a vital role in everyday routine by giving electronic healthcare services and the ability to improve patient care quality. IoHT applications and devices become widely susceptible to cyber-attacks as the tools ar...

A multi-modal graph-based framework for Alzheimer's disease detection.

Scientific reports
We propose a compositional graph-based Machine Learning (ML) framework for Alzheimer's disease (AD) detection that constructs complex ML predictors from modular components. In our directed computational graph, datasets are represented as nodes [Formu...