AIMC Topic: Neural Networks, Computer

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A novel hybrid inception-xception convolutional neural network for efficient plant disease classification and detection.

Scientific reports
Plants are essential at all stages of living things. Plant pests, diseases, and symptoms are most regularly visible in plant leaves and fruits and sometimes within the roots. Yet, their diagnosis by experts in the laboratory is expensive, tedious, an...

Comparison of deep transfer learning models for classification of cervical cancer from pap smear images.

Scientific reports
Cervical cancer is one of the most commonly diagnosed cancers worldwide, and it is particularly prevalent among women living in developing countries. Traditional classification algorithms often require segmentation and feature extraction techniques t...

GGSYOLOv5: Flame recognition method in complex scenes based on deep learning.

PloS one
The continuous development of the field of artificial intelligence, not only makes people's lives more convenient but also plays a role in the supervision and protection of people's lives and property safety. News of the fire is not uncommon, and fir...

Automated recognition and segmentation of lung cancer cytological images based on deep learning.

PloS one
Compared with histological examination of lung cancer, cytology is less invasive and provides better preservation of complete morphology and detail. However, traditional cytological diagnosis requires an experienced pathologist to evaluate all sectio...

Multiparametric MRI-based machine learning system of molecular subgroups and prognosis in medulloblastoma.

European radiology
OBJECTIVES: We aimed to use artificial intelligence to accurately identify molecular subgroups of medulloblastoma (MB), predict clinical outcomes, and incorporate deep learning-based imaging features into the risk stratification.

BCT-Net: semantic-guided breast cancer segmentation on BUS.

Medical & biological engineering & computing
Accurately and swiftly segmenting breast tumors is significant for cancer diagnosis and treatment. Ultrasound imaging stands as one of the widely employed methods in clinical practice. However, due to challenges such as low contrast, blurred boundari...

Graph convolution network-based eeg signal analysis: a review.

Medical & biological engineering & computing
With the advancement of artificial intelligence technology, more and more effective methods are being used to identify and classify Electroencephalography (EEG) signals to address challenges in healthcare and brain-computer interface fields. The appl...

Enhancing surgical precision in squamous cell carcinoma of the head and neck: Hyperspectral imaging and artificial intelligence for improved margin assessment in an ex vivo setting.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
BACKGROUND: Head and neck cancers, constituting 3-5% of all cancer cases, often require surgical resection for optimal outcomes. Achieving complete resection (R0) is crucial, but current methods, relying on white light endoscopy and microscopy, have ...

Sample-efficient and occlusion-robust reinforcement learning for robotic manipulation via multimodal fusion dualization and representation normalization.

Neural networks : the official journal of the International Neural Network Society
Recent advances in visual reinforcement learning (visual RL), which learns from high-dimensional image observations, have narrowed the gap between state-based and image-based training. However, visual RL continues to face significant challenges in ro...

Conditional diffusion model for recommender systems.

Neural networks : the official journal of the International Neural Network Society
Recommender systems are used to filter personalized information for users, as it help avoid information overload. The diffusion model is an advanced deep generative model that has been used in recommender systems due to its effectiveness in reconstru...