AIMC Topic: Deep Learning

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Application of deep machine learning for the radiographic diagnosis of periodontitis.

Clinical oral investigations
OBJECTIVE: Successful application of deep machine learning could reduce time-consuming and labor-intensive clinical work of calculating the amount of radiographic bone loss (RBL) in diagnosing and treatment planning for periodontitis. This study aime...

Changes of Mass Spectra Patterns on a Brain Tissue Section Revealed by Deep Learning with Imaging Mass Spectrometry Data.

Journal of the American Society for Mass Spectrometry
The characteristic patterns of mass spectra in imaging mass spectrometry (IMS) strongly reflect the tissue environment. However, the boundaries formed where different tissue environments collide have not been visually assessed. In this study, IMS and...

Automated Prediction of Kidney Failure in IgA Nephropathy with Deep Learning from Biopsy Images.

Clinical journal of the American Society of Nephrology : CJASN
BACKGROUND AND OBJECTIVES: Digital pathology and artificial intelligence offer new opportunities for automatic histologic scoring. We applied a deep learning approach to IgA nephropathy biopsy images to develop an automatic histologic prognostic scor...

LLDNet: A Lightweight Lane Detection Approach for Autonomous Cars Using Deep Learning.

Sensors (Basel, Switzerland)
Lane detection plays a vital role in making the idea of the autonomous car a reality. Traditional lane detection methods need extensive hand-crafted features and post-processing techniques, which make the models specific feature-oriented, and suscept...

Time-Frequency Mask-Aware Bidirectional LSTM: A Deep Learning Approach for Underwater Acoustic Signal Separation.

Sensors (Basel, Switzerland)
Underwater acoustic signal separation is a key technique for underwater communications. The existing methods are mostly model-based, and cannot accurately characterize the practical underwater acoustic communication environment. They are only suitabl...

Optimisation of Deep Learning Small-Object Detectors with Novel Explainable Verification.

Sensors (Basel, Switzerland)
In this paper, we present a novel methodology based on machine learning for identifying the most appropriate from a set of available state-of-the-art object detectors for a given application. Our particular interest is to develop a road map for ident...

Development of a Smart Chair Sensors System and Classification of Sitting Postures with Deep Learning Algorithms.

Sensors (Basel, Switzerland)
Nowadays in modern societies, a sedentary lifestyle is almost inevitable for a majority of the population. Long hours of sitting, especially in wrong postures, may result in health complications. A smart chair with the capability to identify sitting ...

An Efficient Multi-Scale Convolutional Neural Network Based Multi-Class Brain MRI Classification for SaMD.

Tomography (Ann Arbor, Mich.)
A brain tumor is the growth of abnormal cells in certain brain tissues with a high mortality rate; therefore, it requires high precision in diagnosis, as a minor human judgment can eventually cause severe consequences. Magnetic Resonance Image (MRI) ...

Deep learning to distinguish Best vitelliform macular dystrophy (BVMD) from adult-onset vitelliform macular degeneration (AVMD).

Scientific reports
Initial stages of Best vitelliform macular dystrophy (BVMD) and adult vitelliform macular dystrophy (AVMD) harbor similar blue autofluorescence (BAF) and optical coherence tomography (OCT) features. Nevertheless, BVMD is characterized by a worse fina...